Overview

Brought to you by YData

Dataset statistics

Number of variables51
Number of observations49701
Missing cells849259
Missing cells (%)33.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.7 MiB
Average record size in memory1.1 KiB

Variable types

Numeric32
Categorical15
Text2
DateTime2

Alerts

application_type has constant value "INDIVIDUAL"Constant
addr_state is highly overall correlated with lat and 3 other fieldsHigh correlation
age_of_credit is highly overall correlated with open_il_12mHigh correlation
all_util is highly overall correlated with collections_12_mths_ex_med and 6 other fieldsHigh correlation
annual_inc is highly overall correlated with il_util and 4 other fieldsHigh correlation
collections_12_mths_ex_med is highly overall correlated with all_util and 13 other fieldsHigh correlation
delinq_2yrs is highly overall correlated with mths_since_last_delinq and 1 other fieldsHigh correlation
dti is highly overall correlated with all_util and 2 other fieldsHigh correlation
grade is highly overall correlated with open_il_12m and 1 other fieldsHigh correlation
il_util is highly overall correlated with all_util and 7 other fieldsHigh correlation
initial_list_status is highly overall correlated with open_il_24mHigh correlation
inq_fi is highly overall correlated with collections_12_mths_ex_med and 5 other fieldsHigh correlation
inq_last_12m is highly overall correlated with collections_12_mths_ex_med and 5 other fieldsHigh correlation
inq_last_6mths is highly overall correlated with inq_last_12m and 1 other fieldsHigh correlation
last_pymnt_amnt is highly overall correlated with loan_amntHigh correlation
lat is highly overall correlated with addr_state and 1 other fieldsHigh correlation
lng is highly overall correlated with addr_stateHigh correlation
loan_amnt is highly overall correlated with last_pymnt_amntHigh correlation
loan_status is highly overall correlated with all_util and 13 other fieldsHigh correlation
max_bal_bc is highly overall correlated with collections_12_mths_ex_med and 3 other fieldsHigh correlation
mths_since_last_delinq is highly overall correlated with delinq_2yrs and 2 other fieldsHigh correlation
mths_since_last_major_derog is highly overall correlated with all_util and 3 other fieldsHigh correlation
mths_since_last_record is highly overall correlated with all_util and 12 other fieldsHigh correlation
mths_since_rcnt_il is highly overall correlated with collections_12_mths_ex_med and 1 other fieldsHigh correlation
open_acc is highly overall correlated with open_rv_12m and 2 other fieldsHigh correlation
open_acc_6m is highly overall correlated with annual_inc and 8 other fieldsHigh correlation
open_il_12m is highly overall correlated with age_of_credit and 15 other fieldsHigh correlation
open_il_24m is highly overall correlated with annual_inc and 9 other fieldsHigh correlation
open_il_6m is highly overall correlated with addr_state and 5 other fieldsHigh correlation
open_rv_12m is highly overall correlated with collections_12_mths_ex_med and 5 other fieldsHigh correlation
open_rv_24m is highly overall correlated with collections_12_mths_ex_med and 6 other fieldsHigh correlation
pub_rec is highly overall correlated with open_acc_6m and 2 other fieldsHigh correlation
revol_bal is highly overall correlated with max_bal_bc and 4 other fieldsHigh correlation
revol_util is highly overall correlated with all_utilHigh correlation
sub_grade is highly overall correlated with gradeHigh correlation
term is highly overall correlated with inq_last_12m and 1 other fieldsHigh correlation
tot_coll_amt is highly overall correlated with il_util and 3 other fieldsHigh correlation
tot_cur_bal is highly overall correlated with annual_incHigh correlation
total_acc is highly overall correlated with open_acc and 1 other fieldsHigh correlation
total_bal_il is highly overall correlated with collections_12_mths_ex_med and 4 other fieldsHigh correlation
total_cu_tl is highly overall correlated with collections_12_mths_ex_med and 4 other fieldsHigh correlation
total_rev_hi_lim is highly overall correlated with inq_fi and 1 other fieldsHigh correlation
zip_code is highly overall correlated with addr_state and 1 other fieldsHigh correlation
collections_12_mths_ex_med is highly imbalanced (97.2%)Imbalance
emp_title has 2751 (5.5%) missing valuesMissing
emp_length has 1888 (3.8%) missing valuesMissing
mths_since_last_delinq has 27534 (55.4%) missing valuesMissing
mths_since_last_major_derog has 39904 (80.3%) missing valuesMissing
mths_since_last_record has 43583 (87.7%) missing valuesMissing
open_acc_6m has 49681 (> 99.9%) missing valuesMissing
open_il_6m has 49681 (> 99.9%) missing valuesMissing
open_il_12m has 49681 (> 99.9%) missing valuesMissing
open_il_24m has 49681 (> 99.9%) missing valuesMissing
mths_since_rcnt_il has 49681 (> 99.9%) missing valuesMissing
total_bal_il has 49681 (> 99.9%) missing valuesMissing
il_util has 49682 (> 99.9%) missing valuesMissing
open_rv_12m has 49681 (> 99.9%) missing valuesMissing
open_rv_24m has 49681 (> 99.9%) missing valuesMissing
max_bal_bc has 49681 (> 99.9%) missing valuesMissing
all_util has 49681 (> 99.9%) missing valuesMissing
total_rev_hi_lim has 12670 (25.5%) missing valuesMissing
inq_fi has 49681 (> 99.9%) missing valuesMissing
total_cu_tl has 49681 (> 99.9%) missing valuesMissing
inq_last_12m has 49681 (> 99.9%) missing valuesMissing
tot_coll_amt has 12670 (25.5%) missing valuesMissing
tot_cur_bal has 12670 (25.5%) missing valuesMissing
annual_inc is highly skewed (γ1 = 20.63160709)Skewed
pub_rec has 43766 (88.1%) zerosZeros
delinq_2yrs has 41868 (84.2%) zerosZeros
inq_last_6mths has 23882 (48.1%) zerosZeros
tot_coll_amt has 32683 (65.8%) zerosZeros

Reproduction

Analysis started2024-10-17 20:32:14.366029
Analysis finished2024-10-17 20:33:17.126890
Duration1 minute and 2.76 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

loan_amnt
Real number (ℝ)

HIGH CORRELATION 

Distinct1164
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13651.24
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:17.220790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile3000
Q17425
median12000
Q318650
95-th percentile30000
Maximum35000
Range34500
Interquartile range (IQR)11225

Descriptive statistics

Standard deviation8175.9854
Coefficient of variation (CV)0.59891886
Kurtosis0.078251147
Mean13651.24
Median Absolute Deviation (MAD)5400
Skewness0.82121628
Sum6.784803 × 108
Variance66846737
MonotonicityNot monotonic
2024-10-18T02:03:17.314775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 3488
 
7.0%
12000 2719
 
5.5%
15000 2358
 
4.7%
20000 2200
 
4.4%
8000 1725
 
3.5%
35000 1650
 
3.3%
6000 1598
 
3.2%
5000 1522
 
3.1%
16000 1247
 
2.5%
18000 1135
 
2.3%
Other values (1154) 30059
60.5%
ValueCountFrequency (%)
500 3
 
< 0.1%
750 1
 
< 0.1%
800 1
 
< 0.1%
900 1
 
< 0.1%
1000 194
0.4%
1025 1
 
< 0.1%
1100 4
 
< 0.1%
1125 3
 
< 0.1%
1150 1
 
< 0.1%
1200 103
0.2%
ValueCountFrequency (%)
35000 1650
3.3%
34975 3
 
< 0.1%
34950 1
 
< 0.1%
34900 1
 
< 0.1%
34875 1
 
< 0.1%
34850 1
 
< 0.1%
34800 2
 
< 0.1%
34750 3
 
< 0.1%
34700 1
 
< 0.1%
34675 1
 
< 0.1%

term
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
36 months
38937 
60 months
10764 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters497010
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 36 months
3rd row 36 months
4th row 36 months
5th row 36 months

Common Values

ValueCountFrequency (%)
36 months 38937
78.3%
60 months 10764
 
21.7%

Length

2024-10-18T02:03:17.391799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T02:03:17.490355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
months 49701
50.0%
36 38937
39.2%
60 10764
 
10.8%

Most occurring characters

ValueCountFrequency (%)
99402
20.0%
6 49701
10.0%
t 49701
10.0%
m 49701
10.0%
o 49701
10.0%
n 49701
10.0%
s 49701
10.0%
h 49701
10.0%
3 38937
 
7.8%
0 10764
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 497010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
99402
20.0%
6 49701
10.0%
t 49701
10.0%
m 49701
10.0%
o 49701
10.0%
n 49701
10.0%
s 49701
10.0%
h 49701
10.0%
3 38937
 
7.8%
0 10764
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 497010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
99402
20.0%
6 49701
10.0%
t 49701
10.0%
m 49701
10.0%
o 49701
10.0%
n 49701
10.0%
s 49701
10.0%
h 49701
10.0%
3 38937
 
7.8%
0 10764
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 497010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
99402
20.0%
6 49701
10.0%
t 49701
10.0%
m 49701
10.0%
o 49701
10.0%
n 49701
10.0%
s 49701
10.0%
h 49701
10.0%
3 38937
 
7.8%
0 10764
 
2.2%

grade
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
B
15054 
C
12809 
A
8117 
D
8020 
E
3761 
Other values (2)
1940 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49701
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 15054
30.3%
C 12809
25.8%
A 8117
16.3%
D 8020
16.1%
E 3761
 
7.6%
F 1534
 
3.1%
G 406
 
0.8%

Length

2024-10-18T02:03:17.551663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T02:03:17.612140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
b 15054
30.3%
c 12809
25.8%
a 8117
16.3%
d 8020
16.1%
e 3761
 
7.6%
f 1534
 
3.1%
g 406
 
0.8%

Most occurring characters

ValueCountFrequency (%)
B 15054
30.3%
C 12809
25.8%
A 8117
16.3%
D 8020
16.1%
E 3761
 
7.6%
F 1534
 
3.1%
G 406
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 15054
30.3%
C 12809
25.8%
A 8117
16.3%
D 8020
16.1%
E 3761
 
7.6%
F 1534
 
3.1%
G 406
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 15054
30.3%
C 12809
25.8%
A 8117
16.3%
D 8020
16.1%
E 3761
 
7.6%
F 1534
 
3.1%
G 406
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 15054
30.3%
C 12809
25.8%
A 8117
16.3%
D 8020
16.1%
E 3761
 
7.6%
F 1534
 
3.1%
G 406
 
0.8%

sub_grade
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
B3
3559 
B4
 
3414
C1
 
2930
B2
 
2814
C2
 
2811
Other values (30)
34173 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters99402
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC5
2nd rowC1
3rd rowB5
4th rowB1
5th rowB2

Common Values

ValueCountFrequency (%)
B3 3559
 
7.2%
B4 3414
 
6.9%
C1 2930
 
5.9%
B2 2814
 
5.7%
C2 2811
 
5.7%
B5 2804
 
5.6%
C3 2525
 
5.1%
B1 2463
 
5.0%
C4 2370
 
4.8%
A5 2289
 
4.6%
Other values (25) 21722
43.7%

Length

2024-10-18T02:03:17.668263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b3 3559
 
7.2%
b4 3414
 
6.9%
c1 2930
 
5.9%
b2 2814
 
5.7%
c2 2811
 
5.7%
b5 2804
 
5.6%
c3 2525
 
5.1%
b1 2463
 
5.0%
c4 2370
 
4.8%
a5 2289
 
4.6%
Other values (25) 21722
43.7%

Most occurring characters

ValueCountFrequency (%)
B 15054
15.1%
C 12809
12.9%
4 10368
10.4%
3 10234
10.3%
1 9983
10.0%
2 9946
10.0%
5 9170
9.2%
A 8117
8.2%
D 8020
8.1%
E 3761
 
3.8%
Other values (2) 1940
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99402
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 15054
15.1%
C 12809
12.9%
4 10368
10.4%
3 10234
10.3%
1 9983
10.0%
2 9946
10.0%
5 9170
9.2%
A 8117
8.2%
D 8020
8.1%
E 3761
 
3.8%
Other values (2) 1940
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99402
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 15054
15.1%
C 12809
12.9%
4 10368
10.4%
3 10234
10.3%
1 9983
10.0%
2 9946
10.0%
5 9170
9.2%
A 8117
8.2%
D 8020
8.1%
E 3761
 
3.8%
Other values (2) 1940
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99402
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 15054
15.1%
C 12809
12.9%
4 10368
10.4%
3 10234
10.3%
1 9983
10.0%
2 9946
10.0%
5 9170
9.2%
A 8117
8.2%
D 8020
8.1%
E 3761
 
3.8%
Other values (2) 1940
 
2.0%

emp_title
Text

MISSING 

Distinct30714
Distinct (%)65.4%
Missing2751
Missing (%)5.5%
Memory size3.0 MiB
2024-10-18T02:03:17.801887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length78
Median length44
Mean length17.236635
Min length1

Characters and Unicode

Total characters809260
Distinct characters89
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26458 ?
Unique (%)56.4%

Sample

1st rowAIR RESOURCES BOARD
2nd rowUCLA
3rd rowTarget
4th rowSFMTA
5th rowInternal revenue Service
ValueCountFrequency (%)
manager 3511
 
3.1%
of 2607
 
2.3%
inc 2115
 
1.9%
1037
 
0.9%
sales 851
 
0.8%
service 826
 
0.7%
assistant 776
 
0.7%
director 772
 
0.7%
school 767
 
0.7%
engineer 712
 
0.6%
Other values (16167) 99193
87.7%
2024-10-18T02:03:18.009873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 74239
 
9.2%
68084
 
8.4%
a 57381
 
7.1%
r 56722
 
7.0%
i 52406
 
6.5%
n 52165
 
6.4%
t 47688
 
5.9%
o 45243
 
5.6%
s 37295
 
4.6%
c 30684
 
3.8%
Other values (79) 287353
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 809260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 74239
 
9.2%
68084
 
8.4%
a 57381
 
7.1%
r 56722
 
7.0%
i 52406
 
6.5%
n 52165
 
6.4%
t 47688
 
5.9%
o 45243
 
5.6%
s 37295
 
4.6%
c 30684
 
3.8%
Other values (79) 287353
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 809260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 74239
 
9.2%
68084
 
8.4%
a 57381
 
7.1%
r 56722
 
7.0%
i 52406
 
6.5%
n 52165
 
6.4%
t 47688
 
5.9%
o 45243
 
5.6%
s 37295
 
4.6%
c 30684
 
3.8%
Other values (79) 287353
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 809260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 74239
 
9.2%
68084
 
8.4%
a 57381
 
7.1%
r 56722
 
7.0%
i 52406
 
6.5%
n 52165
 
6.4%
t 47688
 
5.9%
o 45243
 
5.6%
s 37295
 
4.6%
c 30684
 
3.8%
Other values (79) 287353
35.5%

emp_length
Categorical

MISSING 

Distinct11
Distinct (%)< 0.1%
Missing1888
Missing (%)3.8%
Memory size2.7 MiB
10+ years
15069 
2 years
4513 
< 1 year
4001 
3 years
3883 
5 years
3573 
Other values (6)
16774 

Length

Max length9
Median length7
Mean length7.6454939
Min length6

Characters and Unicode

Total characters365554
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row10+ years
3rd row10+ years
4th row3 years
5th row3 years

Common Values

ValueCountFrequency (%)
10+ years 15069
30.3%
2 years 4513
 
9.1%
< 1 year 4001
 
8.1%
3 years 3883
 
7.8%
5 years 3573
 
7.2%
1 year 3276
 
6.6%
4 years 3251
 
6.5%
6 years 3058
 
6.2%
7 years 2882
 
5.8%
8 years 2351
 
4.7%

Length

2024-10-18T02:03:18.088737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 40536
40.7%
10 15069
 
15.1%
1 7277
 
7.3%
year 7277
 
7.3%
2 4513
 
4.5%
4001
 
4.0%
3 3883
 
3.9%
5 3573
 
3.6%
4 3251
 
3.3%
6 3058
 
3.1%
Other values (3) 7189
 
7.2%

Most occurring characters

ValueCountFrequency (%)
51814
14.2%
y 47813
13.1%
r 47813
13.1%
a 47813
13.1%
e 47813
13.1%
s 40536
11.1%
1 22346
6.1%
0 15069
 
4.1%
+ 15069
 
4.1%
2 4513
 
1.2%
Other values (8) 24955
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 365554
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
51814
14.2%
y 47813
13.1%
r 47813
13.1%
a 47813
13.1%
e 47813
13.1%
s 40536
11.1%
1 22346
6.1%
0 15069
 
4.1%
+ 15069
 
4.1%
2 4513
 
1.2%
Other values (8) 24955
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 365554
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
51814
14.2%
y 47813
13.1%
r 47813
13.1%
a 47813
13.1%
e 47813
13.1%
s 40536
11.1%
1 22346
6.1%
0 15069
 
4.1%
+ 15069
 
4.1%
2 4513
 
1.2%
Other values (8) 24955
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 365554
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
51814
14.2%
y 47813
13.1%
r 47813
13.1%
a 47813
13.1%
e 47813
13.1%
s 40536
11.1%
1 22346
6.1%
0 15069
 
4.1%
+ 15069
 
4.1%
2 4513
 
1.2%
Other values (8) 24955
6.8%

home_ownership
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
MORTGAGE
24192 
RENT
21593 
OWN
3868 
OTHER
 
39
NONE
 
8

Length

Max length8
Median length5
Mean length5.8699423
Min length3

Characters and Unicode

Total characters291742
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRENT
2nd rowRENT
3rd rowOWN
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
MORTGAGE 24192
48.7%
RENT 21593
43.4%
OWN 3868
 
7.8%
OTHER 39
 
0.1%
NONE 8
 
< 0.1%
ANY 1
 
< 0.1%

Length

2024-10-18T02:03:18.143883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T02:03:18.198925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mortgage 24192
48.7%
rent 21593
43.4%
own 3868
 
7.8%
other 39
 
0.1%
none 8
 
< 0.1%
any 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G 48384
16.6%
E 45832
15.7%
R 45824
15.7%
T 45824
15.7%
O 28107
9.6%
N 25478
8.7%
A 24193
8.3%
M 24192
8.3%
W 3868
 
1.3%
H 39
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 291742
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 48384
16.6%
E 45832
15.7%
R 45824
15.7%
T 45824
15.7%
O 28107
9.6%
N 25478
8.7%
A 24193
8.3%
M 24192
8.3%
W 3868
 
1.3%
H 39
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 291742
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 48384
16.6%
E 45832
15.7%
R 45824
15.7%
T 45824
15.7%
O 28107
9.6%
N 25478
8.7%
A 24193
8.3%
M 24192
8.3%
W 3868
 
1.3%
H 39
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 291742
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 48384
16.6%
E 45832
15.7%
R 45824
15.7%
T 45824
15.7%
O 28107
9.6%
N 25478
8.7%
A 24193
8.3%
M 24192
8.3%
W 3868
 
1.3%
H 39
 
< 0.1%

annual_inc
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5476
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73827.646
Minimum2000
Maximum4900000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:18.261134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile28000
Q145000
median65000
Q390000
95-th percentile150000
Maximum4900000
Range4898000
Interquartile range (IQR)45000

Descriptive statistics

Standard deviation52515.798
Coefficient of variation (CV)0.71132971
Kurtosis1532.6127
Mean73827.646
Median Absolute Deviation (MAD)20230
Skewness20.631607
Sum3.6693078 × 109
Variance2.757909 × 109
MonotonicityNot monotonic
2024-10-18T02:03:18.320149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 1954
 
3.9%
50000 1631
 
3.3%
65000 1345
 
2.7%
70000 1305
 
2.6%
40000 1273
 
2.6%
80000 1249
 
2.5%
75000 1218
 
2.5%
45000 1187
 
2.4%
55000 1046
 
2.1%
90000 963
 
1.9%
Other values (5466) 36530
73.5%
ValueCountFrequency (%)
2000 1
< 0.1%
4080 1
< 0.1%
4800 2
< 0.1%
5000 1
< 0.1%
6000 2
< 0.1%
7000 2
< 0.1%
7200 1
< 0.1%
7280 1
< 0.1%
7800 2
< 0.1%
7904.04 1
< 0.1%
ValueCountFrequency (%)
4900000 1
< 0.1%
1900000 2
< 0.1%
1440000 1
< 0.1%
1362000 1
< 0.1%
1000000 2
< 0.1%
950000 1
< 0.1%
948000 1
< 0.1%
900000 1
< 0.1%
850000 1
< 0.1%
800000 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Verified
18569 
Not Verified
16642 
Source Verified
14490 

Length

Max length15
Median length12
Mean length11.380173
Min length8

Characters and Unicode

Total characters565606
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowSource Verified
3rd rowSource Verified
4th rowSource Verified
5th rowSource Verified

Common Values

ValueCountFrequency (%)
Verified 18569
37.4%
Not Verified 16642
33.5%
Source Verified 14490
29.2%

Length

2024-10-18T02:03:18.379483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T02:03:18.426409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
verified 49701
61.5%
not 16642
 
20.6%
source 14490
 
17.9%

Most occurring characters

ValueCountFrequency (%)
e 113892
20.1%
i 99402
17.6%
r 64191
11.3%
V 49701
8.8%
f 49701
8.8%
d 49701
8.8%
o 31132
 
5.5%
31132
 
5.5%
N 16642
 
2.9%
t 16642
 
2.9%
Other values (3) 43470
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 565606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 113892
20.1%
i 99402
17.6%
r 64191
11.3%
V 49701
8.8%
f 49701
8.8%
d 49701
8.8%
o 31132
 
5.5%
31132
 
5.5%
N 16642
 
2.9%
t 16642
 
2.9%
Other values (3) 43470
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 565606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 113892
20.1%
i 99402
17.6%
r 64191
11.3%
V 49701
8.8%
f 49701
8.8%
d 49701
8.8%
o 31132
 
5.5%
31132
 
5.5%
N 16642
 
2.9%
t 16642
 
2.9%
Other values (3) 43470
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 565606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 113892
20.1%
i 99402
17.6%
r 64191
11.3%
V 49701
8.8%
f 49701
8.8%
d 49701
8.8%
o 31132
 
5.5%
31132
 
5.5%
N 16642
 
2.9%
t 16642
 
2.9%
Other values (3) 43470
 
7.7%
Distinct103
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size388.4 KiB
Minimum2007-06-01 00:00:00
Maximum2015-12-01 00:00:00
2024-10-18T02:03:18.500942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:18.566612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

loan_status
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
non defaulter
40922 
defaulter
8779 

Length

Max length13
Median length13
Mean length12.293455
Min length9

Characters and Unicode

Total characters610997
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownon defaulter
2nd rownon defaulter
3rd rownon defaulter
4th rownon defaulter
5th rowdefaulter

Common Values

ValueCountFrequency (%)
non defaulter 40922
82.3%
defaulter 8779
 
17.7%

Length

2024-10-18T02:03:18.634726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T02:03:18.682832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
defaulter 49701
54.8%
non 40922
45.2%

Most occurring characters

ValueCountFrequency (%)
e 99402
16.3%
n 81844
13.4%
d 49701
8.1%
u 49701
8.1%
l 49701
8.1%
f 49701
8.1%
a 49701
8.1%
t 49701
8.1%
r 49701
8.1%
40922
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 610997
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 99402
16.3%
n 81844
13.4%
d 49701
8.1%
u 49701
8.1%
l 49701
8.1%
f 49701
8.1%
a 49701
8.1%
t 49701
8.1%
r 49701
8.1%
40922
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 610997
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 99402
16.3%
n 81844
13.4%
d 49701
8.1%
u 49701
8.1%
l 49701
8.1%
f 49701
8.1%
a 49701
8.1%
t 49701
8.1%
r 49701
8.1%
40922
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 610997
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 99402
16.3%
n 81844
13.4%
d 49701
8.1%
u 49701
8.1%
l 49701
8.1%
f 49701
8.1%
a 49701
8.1%
t 49701
8.1%
r 49701
8.1%
40922
6.7%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
debt_consolidation
29251 
credit_card
9889 
other
 
2840
home_improvement
 
2815
major_purchase
 
1183
Other values (9)
3723 

Length

Max length18
Median length18
Mean length14.914529
Min length3

Characters and Unicode

Total characters741267
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsmall_business
2nd rowother
3rd rowdebt_consolidation
4th rowcredit_card
5th rowother

Common Values

ValueCountFrequency (%)
debt_consolidation 29251
58.9%
credit_card 9889
 
19.9%
other 2840
 
5.7%
home_improvement 2815
 
5.7%
major_purchase 1183
 
2.4%
small_business 970
 
2.0%
car 729
 
1.5%
medical 592
 
1.2%
wedding 349
 
0.7%
moving 348
 
0.7%
Other values (4) 735
 
1.5%

Length

2024-10-18T02:03:18.733443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 29251
58.9%
credit_card 9889
 
19.9%
other 2840
 
5.7%
home_improvement 2815
 
5.7%
major_purchase 1183
 
2.4%
small_business 970
 
2.0%
car 729
 
1.5%
medical 592
 
1.2%
wedding 349
 
0.7%
moving 348
 
0.7%
Other values (4) 735
 
1.5%

Most occurring characters

ValueCountFrequency (%)
o 98437
13.3%
d 79647
10.7%
t 74462
10.0%
i 73881
10.0%
n 63504
8.6%
e 54123
7.3%
c 51949
7.0%
a 44681
 
6.0%
_ 44160
 
6.0%
s 34581
 
4.7%
Other values (12) 121842
16.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 741267
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 98437
13.3%
d 79647
10.7%
t 74462
10.0%
i 73881
10.0%
n 63504
8.6%
e 54123
7.3%
c 51949
7.0%
a 44681
 
6.0%
_ 44160
 
6.0%
s 34581
 
4.7%
Other values (12) 121842
16.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 741267
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 98437
13.3%
d 79647
10.7%
t 74462
10.0%
i 73881
10.0%
n 63504
8.6%
e 54123
7.3%
c 51949
7.0%
a 44681
 
6.0%
_ 44160
 
6.0%
s 34581
 
4.7%
Other values (12) 121842
16.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 741267
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 98437
13.3%
d 79647
10.7%
t 74462
10.0%
i 73881
10.0%
n 63504
8.6%
e 54123
7.3%
c 51949
7.0%
a 44681
 
6.0%
_ 44160
 
6.0%
s 34581
 
4.7%
Other values (12) 121842
16.4%

title
Text

Distinct11937
Distinct (%)24.0%
Missing8
Missing (%)< 0.1%
Memory size3.1 MiB
2024-10-18T02:03:18.933110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length80
Median length77
Mean length17.043527
Min length2

Characters and Unicode

Total characters846944
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10349 ?
Unique (%)20.8%

Sample

1st rowreal estate business
2nd rowpersonel
3rd rowConsolidation
4th rowciticard fund
5th rowOther Loan
ValueCountFrequency (%)
debt 21372
18.6%
consolidation 21241
18.5%
credit 8477
 
7.4%
card 7335
 
6.4%
loan 5636
 
4.9%
refinancing 4504
 
3.9%
home 2499
 
2.2%
improvement 1931
 
1.7%
pay 1261
 
1.1%
other 1259
 
1.1%
Other values (5005) 39245
34.2%
2024-10-18T02:03:19.220710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 90438
 
10.7%
n 80280
 
9.5%
i 75026
 
8.9%
66296
 
7.8%
e 65214
 
7.7%
t 65161
 
7.7%
a 56886
 
6.7%
d 46581
 
5.5%
r 35474
 
4.2%
c 33998
 
4.0%
Other values (82) 231590
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 846944
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 90438
 
10.7%
n 80280
 
9.5%
i 75026
 
8.9%
66296
 
7.8%
e 65214
 
7.7%
t 65161
 
7.7%
a 56886
 
6.7%
d 46581
 
5.5%
r 35474
 
4.2%
c 33998
 
4.0%
Other values (82) 231590
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 846944
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 90438
 
10.7%
n 80280
 
9.5%
i 75026
 
8.9%
66296
 
7.8%
e 65214
 
7.7%
t 65161
 
7.7%
a 56886
 
6.7%
d 46581
 
5.5%
r 35474
 
4.2%
c 33998
 
4.0%
Other values (82) 231590
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 846944
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 90438
 
10.7%
n 80280
 
9.5%
i 75026
 
8.9%
66296
 
7.8%
e 65214
 
7.7%
t 65161
 
7.7%
a 56886
 
6.7%
d 46581
 
5.5%
r 35474
 
4.2%
c 33998
 
4.0%
Other values (82) 231590
27.3%

dti
Real number (ℝ)

HIGH CORRELATION 

Distinct3693
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.500422
Minimum0
Maximum39.97
Zeros57
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:19.298526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.23
Q110.7
median16.12
Q321.94
95-th percentile30
Maximum39.97
Range39.97
Interquartile range (IQR)11.24

Descriptive statistics

Standard deviation7.785918
Coefficient of variation (CV)0.47186175
Kurtosis-0.49849268
Mean16.500422
Median Absolute Deviation (MAD)5.61
Skewness0.21833117
Sum820087.48
Variance60.620519
MonotonicityNot monotonic
2024-10-18T02:03:19.370161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
 
0.1%
16.8 50
 
0.1%
19.2 42
 
0.1%
17.63 42
 
0.1%
12 42
 
0.1%
14.4 40
 
0.1%
13.2 39
 
0.1%
10.8 39
 
0.1%
16.08 39
 
0.1%
14.74 38
 
0.1%
Other values (3683) 49273
99.1%
ValueCountFrequency (%)
0 57
0.1%
0.01 1
 
< 0.1%
0.02 3
 
< 0.1%
0.03 1
 
< 0.1%
0.07 1
 
< 0.1%
0.08 4
 
< 0.1%
0.11 4
 
< 0.1%
0.13 1
 
< 0.1%
0.16 3
 
< 0.1%
0.18 1
 
< 0.1%
ValueCountFrequency (%)
39.97 1
< 0.1%
39.96 1
< 0.1%
39.89 2
< 0.1%
39.88 2
< 0.1%
39.86 1
< 0.1%
39.83 1
< 0.1%
39.82 1
< 0.1%
39.72 1
< 0.1%
39.7 1
< 0.1%
39.69 1
< 0.1%
Distinct590
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size388.4 KiB
Minimum1948-01-01 00:00:00
Maximum2012-07-01 00:00:00
2024-10-18T02:03:19.447397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:19.522334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

open_acc
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10.811992
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:19.593718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median10
Q313
95-th percentile20
Maximum49
Range48
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7623564
Coefficient of variation (CV)0.44046985
Kurtosis2.1740872
Mean10.811992
Median Absolute Deviation (MAD)3
Skewness1.0741247
Sum537356
Variance22.680039
MonotonicityNot monotonic
2024-10-18T02:03:19.664116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
9 4785
9.6%
8 4664
 
9.4%
10 4544
 
9.1%
7 4286
 
8.6%
11 4191
 
8.4%
12 3624
 
7.3%
6 3600
 
7.2%
13 3166
 
6.4%
14 2546
 
5.1%
5 2461
 
5.0%
Other values (36) 11833
23.8%
ValueCountFrequency (%)
1 16
 
< 0.1%
2 256
 
0.5%
3 676
 
1.4%
4 1486
 
3.0%
5 2461
5.0%
6 3600
7.2%
7 4286
8.6%
8 4664
9.4%
9 4785
9.6%
10 4544
9.1%
ValueCountFrequency (%)
49 1
 
< 0.1%
48 1
 
< 0.1%
47 2
 
< 0.1%
46 2
 
< 0.1%
45 1
 
< 0.1%
42 1
 
< 0.1%
40 2
 
< 0.1%
39 3
 
< 0.1%
38 2
 
< 0.1%
37 9
< 0.1%

pub_rec
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.13720322
Minimum0
Maximum15
Zeros43766
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:19.727738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42453884
Coefficient of variation (CV)3.0942338
Kurtosis76.180961
Mean0.13720322
Median Absolute Deviation (MAD)0
Skewness5.6481321
Sum6819
Variance0.18023323
MonotonicityNot monotonic
2024-10-18T02:03:19.779955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 43766
88.1%
1 5356
 
10.8%
2 414
 
0.8%
3 102
 
0.2%
4 25
 
0.1%
5 18
 
< 0.1%
6 11
 
< 0.1%
7 3
 
< 0.1%
10 2
 
< 0.1%
8 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 43766
88.1%
1 5356
 
10.8%
2 414
 
0.8%
3 102
 
0.2%
4 25
 
0.1%
5 18
 
< 0.1%
6 11
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
10 2
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 3
 
< 0.1%
6 11
 
< 0.1%
5 18
 
< 0.1%
4 25
 
0.1%
3 102
 
0.2%
2 414
0.8%

revol_bal
Real number (ℝ)

HIGH CORRELATION 

Distinct25784
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15180.724
Minimum0
Maximum586266
Zeros358
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:19.835765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1438
Q15875
median10929
Q319039
95-th percentile38798
Maximum586266
Range586266
Interquartile range (IQR)13164

Descriptive statistics

Standard deviation18576.406
Coefficient of variation (CV)1.2236838
Kurtosis139.96579
Mean15180.724
Median Absolute Deviation (MAD)5969
Skewness8.3293866
Sum7.5449718 × 108
Variance3.4508285 × 108
MonotonicityNot monotonic
2024-10-18T02:03:19.894380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 358
 
0.7%
298 10
 
< 0.1%
5573 10
 
< 0.1%
6858 9
 
< 0.1%
7815 9
 
< 0.1%
6035 9
 
< 0.1%
12177 9
 
< 0.1%
6689 9
 
< 0.1%
4812 9
 
< 0.1%
3122 8
 
< 0.1%
Other values (25774) 49261
99.1%
ValueCountFrequency (%)
0 358
0.7%
1 3
 
< 0.1%
2 5
 
< 0.1%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
586266 1
< 0.1%
555597 1
< 0.1%
552758 1
< 0.1%
487589 1
< 0.1%
465731 1
< 0.1%
451481 1
< 0.1%
434123 1
< 0.1%
401258 1
< 0.1%
393762 1
< 0.1%
388892 1
< 0.1%

revol_util
Real number (ℝ)

HIGH CORRELATION 

Distinct1078
Distinct (%)2.2%
Missing32
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean54.728696
Minimum0
Maximum892.3
Zeros374
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:19.953993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.4
Q136.7
median56.4
Q374.3
95-th percentile92.4
Maximum892.3
Range892.3
Interquartile range (IQR)37.6

Descriptive statistics

Standard deviation25.018053
Coefficient of variation (CV)0.45712861
Kurtosis24.445144
Mean54.728696
Median Absolute Deviation (MAD)18.8
Skewness0.51477983
Sum2718319.6
Variance625.90295
MonotonicityNot monotonic
2024-10-18T02:03:20.012903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 374
 
0.8%
61 96
 
0.2%
67 94
 
0.2%
65.2 94
 
0.2%
56 92
 
0.2%
64.5 91
 
0.2%
46.7 90
 
0.2%
57 89
 
0.2%
53 88
 
0.2%
60 88
 
0.2%
Other values (1068) 48473
97.5%
ValueCountFrequency (%)
0 374
0.8%
0.1 40
 
0.1%
0.16 1
 
< 0.1%
0.2 25
 
0.1%
0.3 34
 
0.1%
0.4 26
 
0.1%
0.46 1
 
< 0.1%
0.5 27
 
0.1%
0.6 19
 
< 0.1%
0.7 19
 
< 0.1%
ValueCountFrequency (%)
892.3 1
< 0.1%
150.7 1
< 0.1%
129.4 1
< 0.1%
127.6 1
< 0.1%
127.4 1
< 0.1%
120.2 1
< 0.1%
117.7 1
< 0.1%
113 1
< 0.1%
112.2 1
< 0.1%
111.9 1
< 0.1%

total_acc
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean24.942596
Minimum2
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:20.072329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q116
median23
Q332
95-th percentile47
Maximum110
Range108
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.703555
Coefficient of variation (CV)0.46921962
Kurtosis0.84885974
Mean24.942596
Median Absolute Deviation (MAD)8
Skewness0.79795943
Sum1239647
Variance136.9732
MonotonicityNot monotonic
2024-10-18T02:03:20.142007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 1799
 
3.6%
19 1778
 
3.6%
20 1770
 
3.6%
23 1763
 
3.5%
21 1738
 
3.5%
17 1719
 
3.5%
18 1716
 
3.5%
24 1679
 
3.4%
16 1675
 
3.4%
25 1665
 
3.4%
Other values (83) 32398
65.2%
ValueCountFrequency (%)
2 10
 
< 0.1%
3 68
 
0.1%
4 197
 
0.4%
5 293
 
0.6%
6 415
 
0.8%
7 557
1.1%
8 769
1.5%
9 832
1.7%
10 970
2.0%
11 1196
2.4%
ValueCountFrequency (%)
110 1
< 0.1%
102 1
< 0.1%
99 1
< 0.1%
96 1
< 0.1%
95 2
< 0.1%
94 1
< 0.1%
93 1
< 0.1%
92 1
< 0.1%
90 1
< 0.1%
89 1
< 0.1%

initial_list_status
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
f
35858 
w
13843 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49701
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowf
2nd rowf
3rd rowf
4th rowf
5th rowf

Common Values

ValueCountFrequency (%)
f 35858
72.1%
w 13843
 
27.9%

Length

2024-10-18T02:03:20.206771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T02:03:20.251945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
f 35858
72.1%
w 13843
 
27.9%

Most occurring characters

ValueCountFrequency (%)
f 35858
72.1%
w 13843
 
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 35858
72.1%
w 13843
 
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 35858
72.1%
w 13843
 
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 35858
72.1%
w 13843
 
27.9%

application_type
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
INDIVIDUAL
49701 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters497010
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDIVIDUAL
2nd rowINDIVIDUAL
3rd rowINDIVIDUAL
4th rowINDIVIDUAL
5th rowINDIVIDUAL

Common Values

ValueCountFrequency (%)
INDIVIDUAL 49701
100.0%

Length

2024-10-18T02:03:20.301718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T02:03:20.343443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
individual 49701
100.0%

Most occurring characters

ValueCountFrequency (%)
I 149103
30.0%
D 99402
20.0%
N 49701
 
10.0%
V 49701
 
10.0%
U 49701
 
10.0%
A 49701
 
10.0%
L 49701
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 497010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 149103
30.0%
D 99402
20.0%
N 49701
 
10.0%
V 49701
 
10.0%
U 49701
 
10.0%
A 49701
 
10.0%
L 49701
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 497010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 149103
30.0%
D 99402
20.0%
N 49701
 
10.0%
V 49701
 
10.0%
U 49701
 
10.0%
A 49701
 
10.0%
L 49701
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 497010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 149103
30.0%
D 99402
20.0%
N 49701
 
10.0%
V 49701
 
10.0%
U 49701
 
10.0%
A 49701
 
10.0%
L 49701
 
10.0%

zip_code
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean833.84928
Minimum601
Maximum985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:20.395612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum601
5-th percentile606
Q1751
median906
Q3926
95-th percentile968
Maximum985
Range384
Interquartile range (IQR)175

Descriptive statistics

Standard deviation119.01034
Coefficient of variation (CV)0.14272404
Kurtosis-0.92860518
Mean833.84928
Median Absolute Deviation (MAD)65
Skewness-0.63300995
Sum41443143
Variance14163.46
MonotonicityNot monotonic
2024-10-18T02:03:20.466738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
606 2498
 
5.0%
917 2110
 
4.2%
921 1987
 
4.0%
926 1896
 
3.8%
913 1698
 
3.4%
925 1596
 
3.2%
956 1473
 
3.0%
920 1390
 
2.8%
951 1307
 
2.6%
802 1268
 
2.6%
Other values (120) 32478
65.3%
ValueCountFrequency (%)
601 1186
2.4%
602 63
 
0.1%
603 58
 
0.1%
606 2498
5.0%
610 164
 
0.3%
612 128
 
0.3%
616 92
 
0.2%
617 146
 
0.3%
622 292
 
0.6%
623 36
 
0.1%
ValueCountFrequency (%)
985 381
0.8%
983 648
1.3%
982 736
1.5%
979 10
 
< 0.1%
976 42
 
0.1%
971 309
0.6%
969 3
 
< 0.1%
968 488
1.0%
965 1
 
< 0.1%
962 5
 
< 0.1%

addr_state
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
CA
22671 
TX
6623 
IL
4812 
CO
 
2133
OK
 
1775
Other values (22)
11687 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters99402
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowIL
2nd rowCA
3rd rowCA
4th rowIL
5th rowCA

Common Values

ValueCountFrequency (%)
CA 22671
45.6%
TX 6623
 
13.3%
IL 4812
 
9.7%
CO 2133
 
4.3%
OK 1775
 
3.6%
WA 1765
 
3.6%
MO 1746
 
3.5%
AZ 1604
 
3.2%
UT 1588
 
3.2%
LA 1322
 
2.7%
Other values (17) 3662
 
7.4%

Length

2024-10-18T02:03:20.531838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 22671
45.6%
tx 6623
 
13.3%
il 4812
 
9.7%
co 2133
 
4.3%
ok 1775
 
3.6%
wa 1765
 
3.6%
mo 1746
 
3.5%
az 1604
 
3.2%
ut 1588
 
3.2%
la 1322
 
2.7%
Other values (17) 3662
 
7.4%

Most occurring characters

ValueCountFrequency (%)
A 28594
28.8%
C 24806
25.0%
T 8214
 
8.3%
X 6623
 
6.7%
L 6137
 
6.2%
O 6016
 
6.1%
I 5303
 
5.3%
K 3079
 
3.1%
W 2010
 
2.0%
M 1751
 
1.8%
Other values (11) 6869
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99402
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 28594
28.8%
C 24806
25.0%
T 8214
 
8.3%
X 6623
 
6.7%
L 6137
 
6.2%
O 6016
 
6.1%
I 5303
 
5.3%
K 3079
 
3.1%
W 2010
 
2.0%
M 1751
 
1.8%
Other values (11) 6869
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99402
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 28594
28.8%
C 24806
25.0%
T 8214
 
8.3%
X 6623
 
6.7%
L 6137
 
6.2%
O 6016
 
6.1%
I 5303
 
5.3%
K 3079
 
3.1%
W 2010
 
2.0%
M 1751
 
1.8%
Other values (11) 6869
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99402
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 28594
28.8%
C 24806
25.0%
T 8214
 
8.3%
X 6623
 
6.7%
L 6137
 
6.2%
O 6016
 
6.1%
I 5303
 
5.3%
K 3079
 
3.1%
W 2010
 
2.0%
M 1751
 
1.8%
Other values (11) 6869
 
6.9%

delinq_2yrs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.23770624
Minimum0
Maximum16
Zeros41868
Zeros (%)84.2%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:20.575458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69641542
Coefficient of variation (CV)2.9297314
Kurtosis46.581383
Mean0.23770624
Median Absolute Deviation (MAD)0
Skewness5.2209005
Sum11814
Variance0.48499444
MonotonicityNot monotonic
2024-10-18T02:03:20.623988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 41868
84.2%
1 5525
 
11.1%
2 1475
 
3.0%
3 434
 
0.9%
4 189
 
0.4%
5 109
 
0.2%
6 49
 
0.1%
7 22
 
< 0.1%
8 10
 
< 0.1%
9 6
 
< 0.1%
Other values (5) 13
 
< 0.1%
ValueCountFrequency (%)
0 41868
84.2%
1 5525
 
11.1%
2 1475
 
3.0%
3 434
 
0.9%
4 189
 
0.4%
5 109
 
0.2%
6 49
 
0.1%
7 22
 
< 0.1%
8 10
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 1
 
< 0.1%
12 5
 
< 0.1%
11 3
 
< 0.1%
10 3
 
< 0.1%
9 6
 
< 0.1%
8 10
 
< 0.1%
7 22
 
< 0.1%
6 49
0.1%
5 109
0.2%

inq_last_6mths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.90034205
Minimum0
Maximum17
Zeros23882
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:20.671335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1493215
Coefficient of variation (CV)1.2765387
Kurtosis5.8060647
Mean0.90034205
Median Absolute Deviation (MAD)1
Skewness1.7869719
Sum44747
Variance1.3209399
MonotonicityNot monotonic
2024-10-18T02:03:20.722074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 23882
48.1%
1 14131
28.4%
2 6957
 
14.0%
3 3237
 
6.5%
4 900
 
1.8%
5 367
 
0.7%
6 136
 
0.3%
7 41
 
0.1%
8 24
 
< 0.1%
9 14
 
< 0.1%
Other values (6) 11
 
< 0.1%
ValueCountFrequency (%)
0 23882
48.1%
1 14131
28.4%
2 6957
 
14.0%
3 3237
 
6.5%
4 900
 
1.8%
5 367
 
0.7%
6 136
 
0.3%
7 41
 
0.1%
8 24
 
< 0.1%
9 14
 
< 0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 5
 
< 0.1%
11 1
 
< 0.1%
10 2
 
< 0.1%
9 14
 
< 0.1%
8 24
 
< 0.1%
7 41
 
0.1%
6 136
0.3%

last_pymnt_amnt
Real number (ℝ)

HIGH CORRELATION 

Distinct45458
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6586.0505
Minimum0
Maximum36257.59
Zeros116
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:20.780769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile112.05
Q1505.34
median4078.09
Q310209.03
95-th percentile22208.16
Maximum36257.59
Range36257.59
Interquartile range (IQR)9703.69

Descriptive statistics

Standard deviation7438.6753
Coefficient of variation (CV)1.1294592
Kurtosis1.6992676
Mean6586.0505
Median Absolute Deviation (MAD)3746.72
Skewness1.4214461
Sum3.273333 × 108
Variance55333890
MonotonicityNot monotonic
2024-10-18T02:03:20.844836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 116
 
0.2%
100 40
 
0.1%
200 32
 
0.1%
50 24
 
< 0.1%
336.9 21
 
< 0.1%
300 20
 
< 0.1%
337.47 17
 
< 0.1%
360.38 17
 
< 0.1%
353.01 15
 
< 0.1%
500 15
 
< 0.1%
Other values (45448) 49384
99.4%
ValueCountFrequency (%)
0 116
0.2%
0.01 5
 
< 0.1%
0.07 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 1
 
< 0.1%
0.13 3
 
< 0.1%
0.14 1
 
< 0.1%
0.17 1
 
< 0.1%
0.18 3
 
< 0.1%
0.19 1
 
< 0.1%
ValueCountFrequency (%)
36257.59 1
< 0.1%
36133.3 1
< 0.1%
36117.51 1
< 0.1%
35997.17 1
< 0.1%
35971.15 1
< 0.1%
35961.64 1
< 0.1%
35926.13 1
< 0.1%
35905.53 1
< 0.1%
35897.76 1
< 0.1%
35883.46 1
< 0.1%

collections_12_mths_ex_med
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Memory size2.5 MiB
0.0
49391 
1.0
 
281
2.0
 
19
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters149076
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49391
99.4%
1.0 281
 
0.6%
2.0 19
 
< 0.1%
3.0 1
 
< 0.1%
(Missing) 9
 
< 0.1%

Length

2024-10-18T02:03:20.903820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T02:03:20.944686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49391
99.4%
1.0 281
 
0.6%
2.0 19
 
< 0.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 99083
66.5%
. 49692
33.3%
1 281
 
0.2%
2 19
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 149076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 99083
66.5%
. 49692
33.3%
1 281
 
0.2%
2 19
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 149076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 99083
66.5%
. 49692
33.3%
1 281
 
0.2%
2 19
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 149076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 99083
66.5%
. 49692
33.3%
1 281
 
0.2%
2 19
 
< 0.1%
3 1
 
< 0.1%

mths_since_last_delinq
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct105
Distinct (%)0.5%
Missing27534
Missing (%)55.4%
Infinite0
Infinite (%)0.0%
Mean35.621464
Minimum0
Maximum141
Zeros143
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:20.992040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q117
median33
Q352
95-th percentile75
Maximum141
Range141
Interquartile range (IQR)35

Descriptive statistics

Standard deviation21.857623
Coefficient of variation (CV)0.61360822
Kurtosis-0.83672627
Mean35.621464
Median Absolute Deviation (MAD)17
Skewness0.3808305
Sum789621
Variance477.75569
MonotonicityNot monotonic
2024-10-18T02:03:21.051579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 421
 
0.8%
15 420
 
0.8%
13 405
 
0.8%
8 401
 
0.8%
12 397
 
0.8%
16 390
 
0.8%
11 386
 
0.8%
23 385
 
0.8%
7 382
 
0.8%
18 381
 
0.8%
Other values (95) 18199
36.6%
(Missing) 27534
55.4%
ValueCountFrequency (%)
0 143
 
0.3%
1 109
 
0.2%
2 176
0.4%
3 172
0.3%
4 229
0.5%
5 257
0.5%
6 362
0.7%
7 382
0.8%
8 401
0.8%
9 421
0.8%
ValueCountFrequency (%)
141 1
< 0.1%
133 1
< 0.1%
120 1
< 0.1%
113 1
< 0.1%
109 1
< 0.1%
108 2
< 0.1%
106 2
< 0.1%
103 1
< 0.1%
101 2
< 0.1%
99 1
< 0.1%

mths_since_last_major_derog
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct128
Distinct (%)1.3%
Missing39904
Missing (%)80.3%
Infinite0
Infinite (%)0.0%
Mean43.653874
Minimum0
Maximum159
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:21.122962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q127
median43
Q360
95-th percentile77
Maximum159
Range159
Interquartile range (IQR)33

Descriptive statistics

Standard deviation21.400446
Coefficient of variation (CV)0.49023018
Kurtosis-0.29232215
Mean43.653874
Median Absolute Deviation (MAD)17
Skewness0.21787581
Sum427677
Variance457.9791
MonotonicityNot monotonic
2024-10-18T02:03:21.213073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 182
 
0.4%
45 174
 
0.4%
47 171
 
0.3%
36 170
 
0.3%
40 169
 
0.3%
46 166
 
0.3%
42 166
 
0.3%
32 164
 
0.3%
48 162
 
0.3%
43 159
 
0.3%
Other values (118) 8114
 
16.3%
(Missing) 39904
80.3%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 20
 
< 0.1%
2 25
 
0.1%
3 19
 
< 0.1%
4 45
0.1%
5 52
0.1%
6 61
0.1%
7 74
0.1%
8 74
0.1%
9 88
0.2%
ValueCountFrequency (%)
159 1
 
< 0.1%
141 1
 
< 0.1%
135 1
 
< 0.1%
133 1
 
< 0.1%
132 2
< 0.1%
131 1
 
< 0.1%
125 3
< 0.1%
124 2
< 0.1%
123 1
 
< 0.1%
122 1
 
< 0.1%

mths_since_last_record
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct121
Distinct (%)2.0%
Missing43583
Missing (%)87.7%
Infinite0
Infinite (%)0.0%
Mean75.950637
Minimum0
Maximum120
Zeros184
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:21.303301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.85
Q154
median79
Q3102
95-th percentile116
Maximum120
Range120
Interquartile range (IQR)48

Descriptive statistics

Standard deviation30.284756
Coefficient of variation (CV)0.39874262
Kurtosis-0.42380009
Mean75.950637
Median Absolute Deviation (MAD)24
Skewness-0.57055747
Sum464666
Variance917.16644
MonotonicityNot monotonic
2024-10-18T02:03:21.384279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 184
 
0.4%
106 105
 
0.2%
107 101
 
0.2%
100 101
 
0.2%
111 100
 
0.2%
110 100
 
0.2%
104 99
 
0.2%
116 99
 
0.2%
112 97
 
0.2%
105 96
 
0.2%
Other values (111) 5036
 
10.1%
(Missing) 43583
87.7%
ValueCountFrequency (%)
0 184
0.4%
1 1
 
< 0.1%
2 5
 
< 0.1%
3 1
 
< 0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
6 5
 
< 0.1%
7 5
 
< 0.1%
8 3
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
120 1
 
< 0.1%
119 55
0.1%
118 95
0.2%
117 82
0.2%
116 99
0.2%
115 69
0.1%
114 85
0.2%
113 85
0.2%
112 97
0.2%
111 100
0.2%

open_acc_6m
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)25.0%
Missing49681
Missing (%)> 99.9%
Memory size2.7 MiB
1.0
11 
2.0
0.0
3.0
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 11
 
< 0.1%
2.0 3
 
< 0.1%
0.0 3
 
< 0.1%
3.0 2
 
< 0.1%
5.0 1
 
< 0.1%
(Missing) 49681
> 99.9%

Length

2024-10-18T02:03:21.449922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T02:03:21.514212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 11
55.0%
2.0 3
 
15.0%
0.0 3
 
15.0%
3.0 2
 
10.0%
5.0 1
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 23
38.3%
. 20
33.3%
1 11
18.3%
2 3
 
5.0%
3 2
 
3.3%
5 1
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23
38.3%
. 20
33.3%
1 11
18.3%
2 3
 
5.0%
3 2
 
3.3%
5 1
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23
38.3%
. 20
33.3%
1 11
18.3%
2 3
 
5.0%
3 2
 
3.3%
5 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23
38.3%
. 20
33.3%
1 11
18.3%
2 3
 
5.0%
3 2
 
3.3%
5 1
 
1.7%

open_il_6m
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)35.0%
Missing49681
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2.9
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:21.571416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile8.15
Maximum11
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5526044
Coefficient of variation (CV)0.88020843
Kurtosis5.1117722
Mean2.9
Median Absolute Deviation (MAD)1
Skewness2.2121493
Sum58
Variance6.5157895
MonotonicityNot monotonic
2024-10-18T02:03:21.630664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 6
 
< 0.1%
1 6
 
< 0.1%
3 4
 
< 0.1%
5 1
 
< 0.1%
11 1
 
< 0.1%
8 1
 
< 0.1%
4 1
 
< 0.1%
(Missing) 49681
> 99.9%
ValueCountFrequency (%)
1 6
< 0.1%
2 6
< 0.1%
3 4
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
8 1
 
< 0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
3 4
< 0.1%
2 6
< 0.1%
1 6
< 0.1%

open_il_12m
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)20.0%
Missing49681
Missing (%)> 99.9%
Memory size2.7 MiB
1.0
10 
0.0
2.0
 
1
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)10.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10
 
< 0.1%
0.0 8
 
< 0.1%
2.0 1
 
< 0.1%
3.0 1
 
< 0.1%
(Missing) 49681
> 99.9%

Length

2024-10-18T02:03:21.692607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T02:03:21.759438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10
50.0%
0.0 8
40.0%
2.0 1
 
5.0%
3.0 1
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 28
46.7%
. 20
33.3%
1 10
 
16.7%
2 1
 
1.7%
3 1
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 28
46.7%
. 20
33.3%
1 10
 
16.7%
2 1
 
1.7%
3 1
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 28
46.7%
. 20
33.3%
1 10
 
16.7%
2 1
 
1.7%
3 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 28
46.7%
. 20
33.3%
1 10
 
16.7%
2 1
 
1.7%
3 1
 
1.7%

open_il_24m
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)25.0%
Missing49681
Missing (%)> 99.9%
Memory size2.7 MiB
1.0
3.0
0.0
2.0
7.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 9
 
< 0.1%
3.0 5
 
< 0.1%
0.0 3
 
< 0.1%
2.0 2
 
< 0.1%
7.0 1
 
< 0.1%
(Missing) 49681
> 99.9%

Length

2024-10-18T02:03:21.820214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T02:03:21.884535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 9
45.0%
3.0 5
25.0%
0.0 3
 
15.0%
2.0 2
 
10.0%
7.0 1
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 23
38.3%
. 20
33.3%
1 9
 
15.0%
3 5
 
8.3%
2 2
 
3.3%
7 1
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23
38.3%
. 20
33.3%
1 9
 
15.0%
3 5
 
8.3%
2 2
 
3.3%
7 1
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23
38.3%
. 20
33.3%
1 9
 
15.0%
3 5
 
8.3%
2 2
 
3.3%
7 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23
38.3%
. 20
33.3%
1 9
 
15.0%
3 5
 
8.3%
2 2
 
3.3%
7 1
 
1.7%

mths_since_rcnt_il
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)75.0%
Missing49681
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean14.25
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:21.945299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q14
median9
Q315.75
95-th percentile38.75
Maximum72
Range71
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation16.424869
Coefficient of variation (CV)1.1526224
Kurtosis7.9153875
Mean14.25
Median Absolute Deviation (MAD)6
Skewness2.5882054
Sum285
Variance269.77632
MonotonicityNot monotonic
2024-10-18T02:03:22.025299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
4 2
 
< 0.1%
9 2
 
< 0.1%
7 2
 
< 0.1%
3 2
 
< 0.1%
15 2
 
< 0.1%
1 1
 
< 0.1%
72 1
 
< 0.1%
14 1
 
< 0.1%
27 1
 
< 0.1%
22 1
 
< 0.1%
Other values (5) 5
 
< 0.1%
(Missing) 49681
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
6 1
< 0.1%
7 2
< 0.1%
9 2
< 0.1%
10 1
< 0.1%
14 1
< 0.1%
15 2
< 0.1%
ValueCountFrequency (%)
72 1
< 0.1%
37 1
< 0.1%
27 1
< 0.1%
22 1
< 0.1%
18 1
< 0.1%
15 2
< 0.1%
14 1
< 0.1%
10 1
< 0.1%
9 2
< 0.1%
7 2
< 0.1%

total_bal_il
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)100.0%
Missing49681
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean34607.9
Minimum4437
Maximum74412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:22.102514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4437
5-th percentile4536.75
Q116769.5
median37551
Q346143.5
95-th percentile69979.3
Maximum74412
Range69975
Interquartile range (IQR)29374

Descriptive statistics

Standard deviation21980.182
Coefficient of variation (CV)0.63512037
Kurtosis-0.9453609
Mean34607.9
Median Absolute Deviation (MAD)17800.5
Skewness0.19629436
Sum692158
Variance4.8312842 × 108
MonotonicityNot monotonic
2024-10-18T02:03:22.165155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
43677 1
 
< 0.1%
21555 1
 
< 0.1%
25766 1
 
< 0.1%
4542 1
 
< 0.1%
39276 1
 
< 0.1%
4437 1
 
< 0.1%
7229 1
 
< 0.1%
69746 1
 
< 0.1%
60460 1
 
< 0.1%
74412 1
 
< 0.1%
Other values (10) 10
 
< 0.1%
(Missing) 49681
> 99.9%
ValueCountFrequency (%)
4437 1
< 0.1%
4542 1
< 0.1%
6042 1
< 0.1%
7229 1
< 0.1%
13240 1
< 0.1%
17946 1
< 0.1%
21555 1
< 0.1%
25766 1
< 0.1%
28811 1
< 0.1%
35826 1
< 0.1%
ValueCountFrequency (%)
74412 1
< 0.1%
69746 1
< 0.1%
63016 1
< 0.1%
60460 1
< 0.1%
47063 1
< 0.1%
45837 1
< 0.1%
43677 1
< 0.1%
42905 1
< 0.1%
40372 1
< 0.1%
39276 1
< 0.1%

il_util
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)100.0%
Missing49682
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean71.242105
Minimum23.3
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:22.240679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23.3
5-th percentile24.83
Q161.05
median83.2
Q388.5
95-th percentile104.22
Maximum108
Range84.7
Interquartile range (IQR)27.45

Descriptive statistics

Standard deviation26.706082
Coefficient of variation (CV)0.37486373
Kurtosis-0.53162772
Mean71.242105
Median Absolute Deviation (MAD)11.3
Skewness-0.78732512
Sum1353.6
Variance713.2148
MonotonicityNot monotonic
2024-10-18T02:03:22.314207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
108 1
 
< 0.1%
74.3 1
 
< 0.1%
83.2 1
 
< 0.1%
25 1
 
< 0.1%
85.7 1
 
< 0.1%
23.3 1
 
< 0.1%
89.2 1
 
< 0.1%
64.6 1
 
< 0.1%
103.8 1
 
< 0.1%
86.5 1
 
< 0.1%
Other values (9) 9
 
< 0.1%
(Missing) 49682
> 99.9%
ValueCountFrequency (%)
23.3 1
< 0.1%
25 1
< 0.1%
25.6 1
< 0.1%
33.1 1
< 0.1%
57.5 1
< 0.1%
64.6 1
< 0.1%
65.8 1
< 0.1%
71.9 1
< 0.1%
74.3 1
< 0.1%
83.2 1
< 0.1%
ValueCountFrequency (%)
108 1
< 0.1%
103.8 1
< 0.1%
91.3 1
< 0.1%
89.2 1
< 0.1%
88.7 1
< 0.1%
88.3 1
< 0.1%
87.8 1
< 0.1%
86.5 1
< 0.1%
85.7 1
< 0.1%
83.2 1
< 0.1%

open_rv_12m
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)35.0%
Missing49681
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2.1
Minimum0
Maximum7
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:22.385006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median2
Q33.25
95-th percentile5.1
Maximum7
Range7
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.9439515
Coefficient of variation (CV)0.92569118
Kurtosis0.46071075
Mean2.1
Median Absolute Deviation (MAD)1.5
Skewness0.89509362
Sum42
Variance3.7789474
MonotonicityNot monotonic
2024-10-18T02:03:22.472529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 5
 
< 0.1%
1 4
 
< 0.1%
2 4
 
< 0.1%
4 3
 
< 0.1%
3 2
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
(Missing) 49681
> 99.9%
ValueCountFrequency (%)
0 5
< 0.1%
1 4
< 0.1%
2 4
< 0.1%
3 2
 
< 0.1%
4 3
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 1
 
< 0.1%
4 3
< 0.1%
3 2
 
< 0.1%
2 4
< 0.1%
1 4
< 0.1%
0 5
< 0.1%

open_rv_24m
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)45.0%
Missing49681
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean3.75
Minimum0
Maximum11
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:22.544149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3.5
Q34.5
95-th percentile9.1
Maximum11
Range11
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.0065717
Coefficient of variation (CV)0.80175247
Kurtosis0.46973624
Mean3.75
Median Absolute Deviation (MAD)2.5
Skewness0.92551584
Sum75
Variance9.0394737
MonotonicityNot monotonic
2024-10-18T02:03:22.606843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 5
 
< 0.1%
1 4
 
< 0.1%
3 3
 
< 0.1%
6 2
 
< 0.1%
0 2
 
< 0.1%
2 1
 
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
(Missing) 49681
> 99.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 4
< 0.1%
2 1
 
< 0.1%
3 3
< 0.1%
4 5
< 0.1%
6 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
6 2
 
< 0.1%
4 5
< 0.1%
3 3
< 0.1%
2 1
 
< 0.1%
1 4
< 0.1%
0 2
 
< 0.1%

max_bal_bc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)100.0%
Missing49681
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean6087.85
Minimum0
Maximum22279
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:22.679011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile564.3
Q12813.75
median5446.5
Q37653
95-th percentile14206.85
Maximum22279
Range22279
Interquartile range (IQR)4839.25

Descriptive statistics

Standard deviation5070.4914
Coefficient of variation (CV)0.83288705
Kurtosis4.7436206
Mean6087.85
Median Absolute Deviation (MAD)2520.5
Skewness1.8253223
Sum121757
Variance25709883
MonotonicityNot monotonic
2024-10-18T02:03:22.736842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
7570 1
 
< 0.1%
13782 1
 
< 0.1%
594 1
 
< 0.1%
7563 1
 
< 0.1%
5844 1
 
< 0.1%
3094 1
 
< 0.1%
3998 1
 
< 0.1%
5836 1
 
< 0.1%
2861 1
 
< 0.1%
1958 1
 
< 0.1%
Other values (10) 10
 
< 0.1%
(Missing) 49681
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
594 1
< 0.1%
1326 1
< 0.1%
1958 1
< 0.1%
2672 1
< 0.1%
2861 1
< 0.1%
3094 1
< 0.1%
3998 1
< 0.1%
5001 1
< 0.1%
5057 1
< 0.1%
ValueCountFrequency (%)
22279 1
< 0.1%
13782 1
< 0.1%
9051 1
< 0.1%
8332 1
< 0.1%
7902 1
< 0.1%
7570 1
< 0.1%
7563 1
< 0.1%
7037 1
< 0.1%
5844 1
< 0.1%
5836 1
< 0.1%

all_util
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)100.0%
Missing49681
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean57.8
Minimum8.7
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:22.791188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.7
5-th percentile26.085
Q141.6
median61.1
Q377
95-th percentile83.395
Maximum89
Range80.3
Interquartile range (IQR)35.4

Descriptive statistics

Standard deviation22.007224
Coefficient of variation (CV)0.38074781
Kurtosis-0.4426698
Mean57.8
Median Absolute Deviation (MAD)17.45
Skewness-0.56423658
Sum1156
Variance484.31789
MonotonicityNot monotonic
2024-10-18T02:03:22.854763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
79.5 1
 
< 0.1%
45.6 1
 
< 0.1%
42.1 1
 
< 0.1%
27 1
 
< 0.1%
83.1 1
 
< 0.1%
50 1
 
< 0.1%
57.4 1
 
< 0.1%
70.4 1
 
< 0.1%
77.6 1
 
< 0.1%
72.6 1
 
< 0.1%
Other values (10) 10
 
< 0.1%
(Missing) 49681
> 99.9%
ValueCountFrequency (%)
8.7 1
< 0.1%
27 1
< 0.1%
27.6 1
< 0.1%
40 1
< 0.1%
40.1 1
< 0.1%
42.1 1
< 0.1%
45.6 1
< 0.1%
50 1
< 0.1%
57.4 1
< 0.1%
58.3 1
< 0.1%
ValueCountFrequency (%)
89 1
< 0.1%
83.1 1
< 0.1%
80.9 1
< 0.1%
79.5 1
< 0.1%
77.6 1
< 0.1%
76.8 1
< 0.1%
72.6 1
< 0.1%
70.4 1
< 0.1%
65.4 1
< 0.1%
63.9 1
< 0.1%

total_rev_hi_lim
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3089
Distinct (%)8.3%
Missing12670
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean29167.814
Minimum0
Maximum677800
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:22.921366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6000
Q113200
median22000
Q336100
95-th percentile74009
Maximum677800
Range677800
Interquartile range (IQR)22900

Descriptive statistics

Standard deviation27695.066
Coefficient of variation (CV)0.94950779
Kurtosis54.47953
Mean29167.814
Median Absolute Deviation (MAD)10400
Skewness4.9692019
Sum1.0801133 × 109
Variance7.670167 × 108
MonotonicityNot monotonic
2024-10-18T02:03:22.988781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15000 143
 
0.3%
11000 139
 
0.3%
8000 138
 
0.3%
13000 136
 
0.3%
13300 135
 
0.3%
11800 134
 
0.3%
14000 131
 
0.3%
11600 129
 
0.3%
13500 128
 
0.3%
12200 127
 
0.3%
Other values (3079) 35691
71.8%
(Missing) 12670
 
25.5%
ValueCountFrequency (%)
0 11
< 0.1%
300 3
 
< 0.1%
400 2
 
< 0.1%
500 7
< 0.1%
600 2
 
< 0.1%
629 1
 
< 0.1%
700 3
 
< 0.1%
800 11
< 0.1%
900 4
 
< 0.1%
1000 13
< 0.1%
ValueCountFrequency (%)
677800 1
< 0.1%
645800 1
< 0.1%
589300 1
< 0.1%
544100 1
< 0.1%
494650 1
< 0.1%
484600 1
< 0.1%
464100 1
< 0.1%
440300 1
< 0.1%
421500 1
< 0.1%
416000 1
< 0.1%

inq_fi
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)30.0%
Missing49681
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1.35
Minimum0
Maximum7
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:23.040774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4.15
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7252002
Coefficient of variation (CV)1.2779261
Kurtosis5.4283899
Mean1.35
Median Absolute Deviation (MAD)1
Skewness2.1270655
Sum27
Variance2.9763158
MonotonicityNot monotonic
2024-10-18T02:03:23.084627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7
 
< 0.1%
1 7
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%
7 1
 
< 0.1%
4 1
 
< 0.1%
(Missing) 49681
> 99.9%
ValueCountFrequency (%)
0 7
< 0.1%
1 7
< 0.1%
2 3
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 3
< 0.1%
1 7
< 0.1%
0 7
< 0.1%

total_cu_tl
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)45.0%
Missing49681
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean3.35
Minimum0
Maximum17
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:23.133909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33.25
95-th percentile15.1
Maximum17
Range17
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation5.3140826
Coefficient of variation (CV)1.5862933
Kurtosis2.1668746
Mean3.35
Median Absolute Deviation (MAD)1
Skewness1.8263843
Sum67
Variance28.239474
MonotonicityNot monotonic
2024-10-18T02:03:23.180169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 8
 
< 0.1%
1 4
 
< 0.1%
3 2
 
< 0.1%
2 1
 
< 0.1%
13 1
 
< 0.1%
6 1
 
< 0.1%
17 1
 
< 0.1%
15 1
 
< 0.1%
4 1
 
< 0.1%
(Missing) 49681
> 99.9%
ValueCountFrequency (%)
0 8
< 0.1%
1 4
< 0.1%
2 1
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
13 1
 
< 0.1%
15 1
 
< 0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
6 1
 
< 0.1%
4 1
 
< 0.1%
3 2
 
< 0.1%
2 1
 
< 0.1%
1 4
< 0.1%
0 8
< 0.1%

inq_last_12m
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)35.0%
Missing49681
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2.5
Minimum-4
Maximum10
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size388.4 KiB
2024-10-18T02:03:23.233158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile-4
Q11
median2
Q33.75
95-th percentile7.15
Maximum10
Range14
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation3.47169
Coefficient of variation (CV)1.388676
Kurtosis0.51134291
Mean2.5
Median Absolute Deviation (MAD)1
Skewness0.23479581
Sum50
Variance12.052632
MonotonicityNot monotonic
2024-10-18T02:03:23.290148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 6
 
< 0.1%
2 6
 
< 0.1%
7 3
 
< 0.1%
-4 2
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
3 1
 
< 0.1%
(Missing) 49681
> 99.9%
ValueCountFrequency (%)
-4 2
 
< 0.1%
1 6
< 0.1%
2 6
< 0.1%
3 1
 
< 0.1%
6 1
 
< 0.1%
7 3
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
7 3
< 0.1%
6 1
 
< 0.1%
3 1
 
< 0.1%
2 6
< 0.1%
1 6
< 0.1%
-4 2
 
< 0.1%

tot_coll_amt
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1709
Distinct (%)4.6%
Missing12670
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean163.31093
Minimum0
Maximum64250
Zeros32683
Zeros (%)65.8%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:23.353331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile442.5
Maximum64250
Range64250
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1360.2336
Coefficient of variation (CV)8.3291034
Kurtosis542.51046
Mean163.31093
Median Absolute Deviation (MAD)0
Skewness19.391047
Sum6047567
Variance1850235.5
MonotonicityNot monotonic
2024-10-18T02:03:23.414315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32683
65.8%
50 58
 
0.1%
100 58
 
0.1%
75 54
 
0.1%
200 31
 
0.1%
90 26
 
0.1%
70 25
 
0.1%
58 25
 
0.1%
55 24
 
< 0.1%
150 22
 
< 0.1%
Other values (1699) 4025
 
8.1%
(Missing) 12670
 
25.5%
ValueCountFrequency (%)
0 32683
65.8%
9 1
 
< 0.1%
15 1
 
< 0.1%
20 1
 
< 0.1%
25 2
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
29 2
 
< 0.1%
30 3
 
< 0.1%
32 1
 
< 0.1%
ValueCountFrequency (%)
64250 1
< 0.1%
60531 1
< 0.1%
55513 1
< 0.1%
41388 1
< 0.1%
37792 1
< 0.1%
36052 1
< 0.1%
35956 1
< 0.1%
34644 1
< 0.1%
33778 1
< 0.1%
31932 1
< 0.1%

tot_cur_bal
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34197
Distinct (%)92.3%
Missing12670
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean144618.95
Minimum0
Maximum2629423
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:23.473768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8750.5
Q128354
median78252
Q3220666.5
95-th percentile445715.5
Maximum2629423
Range2629423
Interquartile range (IQR)192312.5

Descriptive statistics

Standard deviation158922.52
Coefficient of variation (CV)1.0989052
Kurtosis9.8721024
Mean144618.95
Median Absolute Deviation (MAD)63320
Skewness2.180491
Sum5.3553843 × 109
Variance2.5256366 × 1010
MonotonicityNot monotonic
2024-10-18T02:03:23.536736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
< 0.1%
30302 4
 
< 0.1%
13530 4
 
< 0.1%
28409 4
 
< 0.1%
22819 4
 
< 0.1%
15137 4
 
< 0.1%
30491 4
 
< 0.1%
26579 4
 
< 0.1%
19558 4
 
< 0.1%
9558 4
 
< 0.1%
Other values (34187) 36987
74.4%
(Missing) 12670
 
25.5%
ValueCountFrequency (%)
0 8
< 0.1%
2 1
 
< 0.1%
11 1
 
< 0.1%
20 1
 
< 0.1%
51 1
 
< 0.1%
57 1
 
< 0.1%
75 2
 
< 0.1%
79 1
 
< 0.1%
110 1
 
< 0.1%
143 1
 
< 0.1%
ValueCountFrequency (%)
2629423 1
< 0.1%
2547166 1
< 0.1%
1787296 1
< 0.1%
1785763 1
< 0.1%
1769229 1
< 0.1%
1720332 1
< 0.1%
1602181 1
< 0.1%
1578733 1
< 0.1%
1567380 1
< 0.1%
1563515 1
< 0.1%

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.27568
Minimum17.709936
Maximum18.49551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size388.4 KiB
2024-10-18T02:03:23.594679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17.709936
5-th percentile17.747872
Q118.158345
median18.3454
Q318.414292
95-th percentile18.452553
Maximum18.49551
Range0.785574
Interquartile range (IQR)0.255947

Descriptive statistics

Standard deviation0.19375729
Coefficient of variation (CV)0.01060192
Kurtosis1.6154375
Mean18.27568
Median Absolute Deviation (MAD)0.08213
Skewness-1.4989996
Sum908319.56
Variance0.037541887
MonotonicityNot monotonic
2024-10-18T02:03:23.656932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.158345 2498
 
5.0%
18.420674 2110
 
4.2%
18.392282 1987
 
4.0%
18.3454 1896
 
3.8%
18.450002 1698
 
3.4%
18.400296 1596
 
3.2%
18.321137 1473
 
3.0%
18.414292 1390
 
2.8%
18.42753 1307
 
2.6%
18.340862 1268
 
2.6%
Other values (120) 32478
65.3%
ValueCountFrequency (%)
17.709936 1228
2.5%
17.723541 18
 
< 0.1%
17.732228 372
 
0.7%
17.743072 138
 
0.3%
17.744557 50
 
0.1%
17.747872 1094
2.2%
17.768133 492
1.0%
17.963613 59
 
0.1%
17.96577 300
 
0.6%
17.985033 57
 
0.1%
ValueCountFrequency (%)
18.49551 2
 
< 0.1%
18.46832 422
 
0.8%
18.46446 825
1.7%
18.459699 976
2.0%
18.455183 58
 
0.1%
18.452553 806
1.6%
18.451159 210
 
0.4%
18.450002 1698
3.4%
18.448452 144
 
0.3%
18.445328 260
 
0.5%

lng
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-66.071101
Minimum-67.227022
Maximum-64.682933
Zeros0
Zeros (%)0.0%
Negative49701
Negative (%)100.0%
Memory size388.4 KiB
2024-10-18T02:03:23.723913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-67.227022
5-th percentile-66.932911
Q1-66.252183
median-66.088042
Q3-66.038888
95-th percentile-64.880695
Maximum-64.682933
Range2.544089
Interquartile range (IQR)0.213295

Descriptive statistics

Standard deviation0.5549347
Coefficient of variation (CV)-0.0083990532
Kurtosis0.64683447
Mean-66.071101
Median Absolute Deviation (MAD)0.141865
Skewness0.69316908
Sum-3283799.8
Variance0.30795252
MonotonicityNot monotonic
2024-10-18T02:03:23.800797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-66.932911 2498
 
5.0%
-66.050105 2110
 
4.2%
-66.088555 1987
 
4.0%
-66.051545 1896
 
3.8%
-66.042656 1698
 
3.4%
-66.050602 1596
 
3.2%
-66.170419 1473
 
3.0%
-66.088042 1390
 
2.8%
-66.253789 1307
 
2.6%
-64.923479 1268
 
2.6%
Other values (120) 32478
65.3%
ValueCountFrequency (%)
-67.227022 19
 
< 0.1%
-67.175597 63
 
0.1%
-67.153993 292
0.6%
-67.153897 36
 
0.1%
-67.132502 9
 
< 0.1%
-67.125135 164
0.3%
-67.119887 58
 
0.1%
-67.116199 401
0.8%
-67.098671 2
 
< 0.1%
-67.079574 25
 
0.1%
ValueCountFrequency (%)
-64.682933 50
 
0.1%
-64.68657 138
 
0.3%
-64.736533 23
 
< 0.1%
-64.745932 18
 
< 0.1%
-64.750099 1228
2.5%
-64.77076 372
 
0.7%
-64.78672 17
 
< 0.1%
-64.807852 492
 
1.0%
-64.880695 1094
2.2%
-64.923479 1268
2.6%

age_of_credit
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.292328
Minimum0
Maximum66
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size194.3 KiB
2024-10-18T02:03:23.885420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q111
median14
Q319
95-th percentile29
Maximum66
Range66
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.1153725
Coefficient of variation (CV)0.46529034
Kurtosis2.0635803
Mean15.292328
Median Absolute Deviation (MAD)4
Skewness1.1580392
Sum760044
Variance50.628526
MonotonicityNot monotonic
2024-10-18T02:03:23.954554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 3774
 
7.6%
12 3763
 
7.6%
11 3540
 
7.1%
14 3520
 
7.1%
15 2999
 
6.0%
10 2852
 
5.7%
16 2611
 
5.3%
17 2307
 
4.6%
9 2234
 
4.5%
18 2079
 
4.2%
Other values (53) 20022
40.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 5
 
< 0.1%
2 17
 
< 0.1%
3 235
 
0.5%
4 666
 
1.3%
5 1055
2.1%
6 1390
2.8%
7 1786
3.6%
8 1958
3.9%
9 2234
4.5%
ValueCountFrequency (%)
66 1
 
< 0.1%
61 1
 
< 0.1%
60 2
 
< 0.1%
59 1
 
< 0.1%
58 2
 
< 0.1%
57 1
 
< 0.1%
56 1
 
< 0.1%
55 3
< 0.1%
54 4
< 0.1%
53 5
< 0.1%

Interactions

2024-10-18T02:03:12.589824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:19.729532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:21.502035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:22.946417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:24.719809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:26.503483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:27.940345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:29.810668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:31.228310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:32.650248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:35.622466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:37.741472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:39.235162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:40.732335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:42.189809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:44.436511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:45.867036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:47.063653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:48.343328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:49.609994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:51.750332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:53.024513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:54.246732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:55.498832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:56.863477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:58.487020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:02.179773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:03.587222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:05.002295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:06.678993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:09.071865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:10.957914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:12.639378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:19.796022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:21.549078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:22.997857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:24.766512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:26.552461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:27.988259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:29.855544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:31.276029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:32.744820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:35.671770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:37.790065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:39.281756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:40.779451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:42.237467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:44.484697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:45.903992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:47.102925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:48.380983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:49.647014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:51.790460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:53.063102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:54.282171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:55.539470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:56.915225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:58.538857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:02.222681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:03.630676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:05.059305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:06.751328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:09.133205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:11.012762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:12.683423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:19.841392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:21.592075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:23.043241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:24.811949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:26.596732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:28.032170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:29.896659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:31.318632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:32.829859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:35.720664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:37.836264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:39.331561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:40.824950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:42.281721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:44.536059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:45.940142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:47.141919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:48.420317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:49.686754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:51.829499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:53.103513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:54.320784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:55.581945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:56.962375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:58.586192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:02.265954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:03.669416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:05.118242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:06.809417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:09.188370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:11.060374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:12.735164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:19.888944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:21.638366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:23.388374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:24.861176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:26.645240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:28.079357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:29.942537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:31.365264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:33.446591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:35.772848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:37.885644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:39.381400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:40.871185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:42.328855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:44.583009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:45.974961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:47.181424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:48.455356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:49.728989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:51.866034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:53.141926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:54.356211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:55.621493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:57.016703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:58.630774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:02.308807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:03.710125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:05.172817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:06.863074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:09.260613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:11.114882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:12.779468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:19.933026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:21.678746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:23.436096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:24.903800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:26.689531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:28.123884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:29.986679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:31.406768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:33.532181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:35.816882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-10-18T02:02:35.369914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:37.595255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:39.090851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:40.588547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:42.050876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:44.300115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:45.734412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:46.945331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:48.223158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:49.489415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:51.633167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:52.904837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:54.129647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:55.380567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:56.733530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:58.305710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:02.053477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:03.432359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:04.844136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:06.469430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:08.896184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:10.786769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:12.453263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:13.971319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:21.408727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:22.857403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:24.628497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:26.414001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:27.848869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:29.723889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:31.141908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:32.546606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:35.460670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:37.646473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:39.140237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:40.638982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:42.101116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:44.350067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:45.781427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:46.987903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:48.266639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:49.531310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:51.673196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:52.946306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:54.169729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:55.422077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:56.778382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:58.372678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:02.093753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:03.482507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:04.896665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:06.547207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:08.962698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:10.846112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:12.502037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:14.017091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:21.454646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:22.901958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:24.671070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:26.456898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:27.891776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:29.765445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:31.183259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:32.590143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:35.546530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:37.692539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:39.185263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:40.684782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:42.144130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:44.392759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:45.823037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:47.026789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:48.304945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:49.571325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:51.709042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:52.986186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:54.207403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:55.459208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:56.821254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:02:58.429464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:02.136937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:03.539188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:04.946205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:06.619079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:09.018096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:10.899985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-18T02:03:12.544513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-18T02:03:24.037450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
addr_stateage_of_creditall_utilannual_inccollections_12_mths_ex_meddelinq_2yrsdtiemp_lengthgradehome_ownershipil_utilinitial_list_statusinq_fiinq_last_12minq_last_6mthslast_pymnt_amntlatlngloan_amntloan_statusmax_bal_bcmths_since_last_delinqmths_since_last_major_derogmths_since_last_recordmths_since_rcnt_ilopen_accopen_acc_6mopen_il_12mopen_il_24mopen_il_6mopen_rv_12mopen_rv_24mpub_recpurposerevol_balrevol_utilsub_gradetermtot_coll_amttot_cur_baltotal_acctotal_bal_iltotal_cu_tltotal_rev_hi_limverification_statuszip_code
addr_state1.0000.0180.3370.0000.0220.0000.0440.0270.0260.1170.4010.0300.0000.1670.0280.0230.5850.6440.0210.0390.0000.0100.0050.0610.0850.0250.0000.0000.0000.5060.0000.0000.0000.0200.0000.0160.0270.0350.0000.0560.0380.0000.2840.0000.0230.759
age_of_credit0.0181.0000.3440.2930.0050.1120.0410.0990.0360.1040.3790.0750.2750.1560.0030.155-0.0290.0110.2000.0430.146-0.046-0.032-0.1130.2320.1820.4250.5820.414-0.3880.1460.3680.1190.0360.2710.0050.0310.0770.0240.2100.3950.1330.1930.2750.086-0.018
all_util0.3370.3441.000-0.3321.000-0.0020.5020.2830.0000.0000.6770.2480.2900.026-0.124-0.2140.097-0.114-0.2311.0000.395-0.164-0.5001.000-0.2730.1110.0000.0000.0000.387-0.350-0.1060.1730.3570.4530.7100.1350.000-0.438-0.116-0.1510.4680.073-0.2510.103-0.072
annual_inc0.0000.293-0.3321.0000.0000.093-0.1950.0090.0080.012-0.5470.000-0.0420.2100.0700.3220.0430.0170.4750.0070.262-0.085-0.108-0.0160.1840.2521.0001.0001.000-0.2470.1100.018-0.0210.0100.3730.0440.0070.001-0.0140.5200.3680.023-0.0480.3770.0090.031
collections_12_mths_ex_med0.0220.0051.0000.0001.0000.0270.0050.0000.0150.0001.0000.0281.0001.0000.0000.0110.0020.0050.0100.0001.0000.0200.0680.0001.0000.0101.0001.0001.0001.0001.0001.0000.0050.0000.0000.0100.0160.0100.1360.0000.0101.0001.0000.0000.0230.000
delinq_2yrs0.0000.112-0.0020.0930.0271.000-0.0050.0120.0330.0100.3050.026-0.152-0.1790.0250.0260.0040.0050.0140.0150.048-0.801-0.553-0.0150.3480.0650.0000.0000.0000.0170.2270.318-0.0310.010-0.048-0.0200.0370.0050.0350.0840.137-0.081-0.327-0.0490.0110.003
dti0.0440.0410.502-0.1950.005-0.0051.0000.0180.0730.0180.3790.0620.2800.1250.0020.009-0.0690.0150.0580.1140.1280.0030.0270.064-0.0230.3260.4400.6710.4640.4340.0750.199-0.0420.0710.2630.2090.0630.068-0.0240.0900.2280.5280.4490.1150.111-0.063
emp_length0.0270.0990.2830.0090.0000.0120.0181.0000.0070.0990.0000.0500.0000.0000.0100.0390.0190.0250.0460.0200.0000.0250.0270.0460.0000.0270.2310.0000.0000.3730.4170.0000.0040.0270.0110.0070.0100.0730.0000.0350.0600.0000.0000.0180.0720.046
grade0.0260.0360.0000.0080.0150.0330.0730.0071.0000.0480.0000.0410.0000.1890.0810.0920.0090.0090.1070.2270.0000.0220.0240.0740.2980.0280.0000.5060.0000.0000.2790.0000.0160.0820.0140.1211.0000.4820.0150.0500.0280.1720.3230.0710.1500.021
home_ownership0.1170.1040.0000.0120.0000.0100.0180.0990.0481.0000.0000.0380.0000.3500.0310.0780.0910.0890.0850.0770.1480.0210.0000.0560.0000.0730.0000.0000.3860.2660.0000.1320.0000.0870.0420.0150.0530.0960.0000.1770.1120.1730.1000.0550.0650.159
il_util0.4010.3790.677-0.5471.0000.3050.3790.0000.0000.0001.0000.0000.2070.030-0.019-0.046-0.258-0.079-0.0501.0000.142-0.564-0.5331.000-0.2090.4120.2760.0000.0000.2150.1180.3510.2820.0000.1350.1330.2130.000-0.574-0.0440.1750.3770.0990.0070.298-0.381
initial_list_status0.0300.0750.2480.0000.0280.0260.0620.0500.0410.0380.0001.0000.0000.0000.0300.1940.0170.0220.0800.0180.0000.0110.0250.1620.0000.0760.0000.0000.5660.0000.3260.0000.0290.0930.0070.0230.0550.0870.0100.0200.0760.1840.4470.0350.0780.038
inq_fi0.0000.2750.290-0.0421.000-0.1520.2800.0000.0000.0000.2070.0001.0000.4770.154-0.0960.007-0.307-0.1131.000-0.370-0.073-0.3811.000-0.305-0.1050.5170.5000.3910.0410.0220.175-0.1820.000-0.2630.2220.0000.000-0.1570.005-0.1590.3480.223-0.5700.000-0.357
inq_last_12m0.1670.1560.0260.2101.000-0.1790.1250.0000.1890.3500.0300.0000.4771.0000.6940.0950.055-0.4790.0971.000-0.0940.3530.1131.0000.1010.2270.0000.0000.000-0.1490.4590.5160.2530.000-0.2620.1060.0000.692-0.0310.0050.2100.0580.063-0.4690.0000.152
inq_last_6mths0.0280.003-0.1240.0700.0000.0250.0020.0100.0810.031-0.0190.0300.1540.6941.000-0.020-0.052-0.042-0.0120.054-0.301-0.002-0.002-0.1200.0410.1120.2410.6860.0000.1340.4930.4660.0470.056-0.045-0.0880.0900.0280.0230.0670.1360.0900.4050.0100.048-0.075
last_pymnt_amnt0.0230.155-0.2140.3220.0110.0260.0090.0390.0920.078-0.0460.194-0.0960.095-0.0201.000-0.0040.0310.5020.4830.197-0.004-0.0090.005-0.0140.1540.0000.0000.195-0.0300.1740.0870.0290.0580.210-0.0160.0790.388-0.0110.2510.2060.0380.1320.2550.1800.010
lat0.585-0.0290.0970.0430.0020.004-0.0690.0190.0090.091-0.2580.0170.0070.055-0.052-0.0041.0000.0440.0110.0160.109-0.008-0.026-0.0520.388-0.0400.1720.4470.307-0.023-0.247-0.102-0.0100.0200.0010.0080.0130.014-0.063-0.062-0.0840.033-0.0560.0000.0210.547
lng0.6440.011-0.1140.0170.0050.0050.0150.0250.0090.089-0.0790.022-0.307-0.479-0.0420.0310.0441.0000.0120.0280.243-0.007-0.0310.0120.0120.0010.1880.2590.3640.316-0.141-0.1560.0160.0200.0010.0160.0100.0240.0040.0460.0060.3130.138-0.0180.0220.285
loan_amnt0.0210.200-0.2310.4750.0100.0140.0580.0460.1070.085-0.0500.080-0.1130.097-0.0120.5020.0110.0121.0000.0510.191-0.035-0.0640.0950.0010.2220.0000.0000.130-0.0220.1910.096-0.0800.1060.4560.0970.0950.413-0.0620.3240.2450.0410.1370.4370.2650.004
loan_status0.0390.0431.0000.0070.0000.0150.1140.0200.2270.0771.0000.0181.0001.0000.0540.4830.0160.0280.0511.0001.0000.0210.0120.1151.0000.0131.0001.0001.0001.0001.0001.0000.0040.0650.0070.0460.2320.1330.0080.0820.0411.0001.0000.0430.0600.053
max_bal_bc0.0000.1460.3950.2621.0000.0480.1280.0000.0000.1480.1420.000-0.370-0.094-0.3010.1970.1090.2430.1911.0001.000-0.0910.167-1.0000.3270.0900.0000.0000.000-0.093-0.300-0.1840.1730.0000.8180.4980.0930.0000.0770.0270.0720.045-0.1780.4360.0000.321
mths_since_last_delinq0.010-0.046-0.164-0.0850.020-0.8010.0030.0250.0220.021-0.5640.011-0.0730.353-0.002-0.004-0.008-0.007-0.0350.021-0.0911.0000.733-0.002-0.032-0.0440.0000.0000.000-0.2260.089-0.0590.1120.005-0.0180.0180.0100.0000.054-0.112-0.060-0.1640.467-0.0430.000-0.002
mths_since_last_major_derog0.005-0.032-0.500-0.1080.068-0.5530.0270.0270.0240.000-0.5330.025-0.3810.113-0.002-0.009-0.026-0.031-0.0640.0120.1670.7331.0000.0850.0590.0050.0000.0000.000-0.1470.051-0.2510.1550.000-0.045-0.0320.0000.0000.091-0.094-0.074-0.2000.261-0.0370.024-0.053
mths_since_last_record0.061-0.1131.000-0.0160.000-0.0150.0640.0460.0740.0561.0000.1621.0001.000-0.1200.005-0.0520.0120.0950.115-1.000-0.0020.0851.000NaN0.0141.0001.0001.0001.0001.0001.000-0.0930.0480.2160.1500.0760.095-0.0620.105-0.2731.0001.0000.1450.222-0.075
mths_since_rcnt_il0.0850.232-0.2730.1841.0000.348-0.0230.0000.2980.000-0.2090.000-0.3050.1010.041-0.0140.3880.0120.0011.0000.327-0.0320.059NaN1.0000.3670.0000.0000.278-0.3970.2410.333-0.1160.3730.4610.1050.0000.0000.1700.2320.367-0.078-0.1010.3760.0000.342
open_acc0.0250.1820.1110.2520.0100.0650.3260.0270.0280.0730.4120.076-0.1050.2270.1120.154-0.0400.0010.2220.0130.090-0.0440.0050.0140.3671.0000.1440.2700.0000.1890.5480.682-0.0100.0500.364-0.1150.0340.0690.0340.3250.6610.2270.1820.4710.063-0.067
open_acc_6m0.0000.4250.0001.0001.0000.0000.4400.2310.0000.0000.2760.0000.5170.0000.2410.0000.1720.1880.0001.0000.0000.0000.0001.0000.0000.1441.0000.5170.3720.1460.4650.4131.0000.0001.0000.0000.2380.0001.0000.0000.0000.1600.3960.0840.3700.000
open_il_12m0.0000.5820.0001.0001.0000.0000.6710.0000.5060.0000.0000.0000.5000.0000.6860.0000.4470.2590.0001.0000.0000.0000.0001.0000.0000.2700.5171.0000.5800.0000.6240.4611.0000.0001.0000.0000.1540.0001.0000.0000.2260.3020.7020.0960.0000.000
open_il_24m0.0000.4140.0001.0001.0000.0000.4640.0000.0000.3860.0000.5660.3910.0000.0000.1950.3070.3640.1301.0000.0000.0000.0001.0000.2780.0000.3720.5801.0000.0000.3320.3801.0000.2211.0000.0000.0000.0741.0000.0000.0000.0000.5400.0000.0000.277
open_il_6m0.506-0.3880.387-0.2471.0000.0170.4340.3730.0000.2660.2150.0000.041-0.1490.134-0.030-0.0230.316-0.0221.000-0.093-0.226-0.1471.000-0.3970.1890.1460.0000.0001.000-0.130-0.216-0.0890.000-0.081-0.0360.3330.882-0.374-0.084-0.0320.6420.128-0.0260.000-0.210
open_rv_12m0.0000.146-0.3500.1101.0000.2270.0750.4170.2790.0000.1180.3260.0220.4590.4930.174-0.247-0.1410.1911.000-0.3000.0890.0511.0000.2410.5480.4650.6240.332-0.1301.0000.8940.0290.000-0.350-0.4370.0000.000-0.075-0.1210.467-0.028-0.018-0.1100.000-0.177
open_rv_24m0.0000.368-0.1060.0181.0000.3180.1990.0000.0000.1320.3510.0000.1750.5160.4660.087-0.102-0.1560.0961.000-0.184-0.059-0.2511.0000.3330.6820.4130.4610.380-0.2160.8941.0000.0290.000-0.155-0.2150.0000.000-0.2860.0880.5410.0690.054-0.1330.000-0.125
pub_rec0.0000.1190.173-0.0210.005-0.031-0.0420.0040.0160.0000.2820.029-0.1820.2530.0470.029-0.0100.016-0.0800.0040.1730.1120.155-0.093-0.116-0.0101.0001.0001.000-0.0890.0290.0291.0000.000-0.186-0.0770.0120.0000.088-0.1080.050-0.1730.015-0.2050.0350.016
purpose0.0200.0360.3570.0100.0000.0100.0710.0270.0820.0870.0000.0930.0000.0000.0560.0580.0200.0200.1060.0650.0000.0050.0000.0480.3730.0500.0000.0000.2210.0000.0000.0000.0001.0000.0200.0290.0600.0840.0000.0400.0420.0000.0000.0270.0700.029
revol_bal0.0000.2710.4530.3730.000-0.0480.2630.0110.0140.0420.1350.007-0.263-0.262-0.0450.2100.0010.0010.4560.0070.818-0.018-0.0450.2160.4610.3641.0001.0001.000-0.081-0.350-0.155-0.1860.0201.0000.4230.0180.018-0.1420.3760.2940.171-0.1180.7630.035-0.017
revol_util0.0160.0050.7100.0440.010-0.0200.2090.0070.1210.0150.1330.0230.2220.106-0.088-0.0160.0080.0160.0970.0460.4980.018-0.0320.1500.105-0.1150.0000.0000.000-0.036-0.437-0.215-0.0770.0290.4231.0000.1220.016-0.1030.086-0.0930.161-0.122-0.1720.0280.025
sub_grade0.0270.0310.1350.0070.0160.0370.0630.0101.0000.0530.2130.0550.0000.0000.0900.0790.0130.0100.0950.2320.0930.0100.0000.0760.0000.0340.2380.1540.0000.3330.0000.0000.0120.0600.0180.1221.0000.4960.0210.0510.0270.3830.3800.0660.1580.012
term0.0350.0770.0000.0010.0100.0050.0680.0730.4820.0960.0000.0870.0000.6920.0280.3880.0140.0240.4130.1330.0000.0000.0000.0950.0000.0690.0000.0000.0740.8820.0000.0000.0000.0840.0180.0160.4961.0000.0110.0960.0930.0000.0000.0350.2740.054
tot_coll_amt0.0000.024-0.438-0.0140.1360.035-0.0240.0000.0150.000-0.5740.010-0.157-0.0310.023-0.011-0.0630.004-0.0620.0080.0770.0540.091-0.0620.1700.0341.0001.0001.000-0.374-0.075-0.2860.0880.000-0.142-0.1030.0210.0111.000-0.0230.041-0.469-0.063-0.1070.000-0.062
tot_cur_bal0.0560.210-0.1160.5200.0000.0840.0900.0350.0500.177-0.0440.0200.0050.0050.0670.251-0.0620.0460.3240.0820.027-0.112-0.0940.1050.2320.3250.0000.0000.000-0.084-0.1210.088-0.1080.0400.3760.0860.0510.096-0.0231.0000.3840.2020.1990.3550.083-0.028
total_acc0.0380.395-0.1510.3680.0100.1370.2280.0600.0280.1120.1750.076-0.1590.2100.1360.206-0.0840.0060.2450.0410.072-0.060-0.074-0.2730.3670.6610.0000.2260.000-0.0320.4670.5410.0500.0420.294-0.0930.0270.0930.0410.3841.0000.0810.3280.3560.088-0.086
total_bal_il0.0000.1330.4680.0231.000-0.0810.5280.0000.1720.1730.3770.1840.3480.0580.0900.0380.0330.3130.0411.0000.045-0.164-0.2001.000-0.0780.2270.1600.3020.0000.642-0.0280.069-0.1730.0000.1710.1610.3830.000-0.4690.2020.0811.0000.3920.1850.159-0.318
total_cu_tl0.2840.1930.073-0.0481.000-0.3270.4490.0000.3230.1000.0990.4470.2230.0630.4050.132-0.0560.1380.1371.000-0.1780.4670.2611.000-0.1010.1820.3960.7020.5400.128-0.0180.0540.0150.000-0.118-0.1220.3800.000-0.0630.1990.3280.3921.000-0.0270.448-0.389
total_rev_hi_lim0.0000.275-0.2510.3770.000-0.0490.1150.0180.0710.0550.0070.035-0.570-0.4690.0100.2550.000-0.0180.4370.0430.436-0.043-0.0370.1450.3760.4710.0840.0960.000-0.026-0.110-0.133-0.2050.0270.763-0.1720.0660.035-0.1070.3550.3560.185-0.0271.0000.050-0.038
verification_status0.0230.0860.1030.0090.0230.0110.1110.0720.1500.0650.2980.0780.0000.0000.0480.1800.0210.0220.2650.0600.0000.0000.0240.2220.0000.0630.3700.0000.0000.0000.0000.0000.0350.0700.0350.0280.1580.2740.0000.0830.0880.1590.4480.0501.0000.048
zip_code0.759-0.018-0.0720.0310.0000.003-0.0630.0460.0210.159-0.3810.038-0.3570.152-0.0750.0100.5470.2850.0040.0530.321-0.002-0.053-0.0750.342-0.0670.0000.0000.277-0.210-0.177-0.1250.0160.029-0.0170.0250.0120.054-0.062-0.028-0.086-0.318-0.389-0.0380.0481.000

Missing values

2024-10-18T02:03:15.797384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-18T02:03:16.131885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-18T02:03:16.875306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

loan_amnttermgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspurposetitledtiearliest_cr_lineopen_accpub_recrevol_balrevol_utiltotal_accinitial_list_statusapplication_typezip_codeaddr_statedelinq_2yrsinq_last_6mthslast_pymnt_amntcollections_12_mths_ex_medmths_since_last_delinqmths_since_last_major_derogmths_since_last_recordopen_acc_6mopen_il_6mopen_il_12mopen_il_24mmths_since_rcnt_iltotal_bal_ilil_utilopen_rv_12mopen_rv_24mmax_bal_bcall_utiltotal_rev_hi_liminq_fitotal_cu_tlinq_last_12mtot_coll_amttot_cur_ballatlngage_of_credit
02400.036 monthsCC5NaN10+ yearsRENT12252.00Not Verified2011-12-01non defaultersmall_businessreal estate business8.722001-11-012.00.02956.098.510.0fINDIVIDUAL606IL0.02.0649.910.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN18.158345-66.93291110
110000.036 monthsCC1AIR RESOURCES BOARD10+ yearsRENT49200.00Source Verified2011-12-01non defaulterotherpersonel20.001996-02-0110.00.05598.021.037.0fINDIVIDUAL917CA0.01.0357.480.035.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN18.420674-66.05010515
212000.036 monthsBB5UCLA10+ yearsOWN75000.00Source Verified2011-12-01non defaulterdebt_consolidationConsolidation10.781989-10-0112.00.023336.067.134.0fINDIVIDUAL913CA0.00.06315.300.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN18.450002-66.04265622
33000.036 monthsBB1Target3 yearsRENT15000.00Source Verified2011-12-01non defaultercredit_cardciticard fund12.562003-07-0111.00.07323.043.111.0fINDIVIDUAL606IL0.02.0102.430.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN18.158345-66.9329118
410000.036 monthsBB2SFMTA3 yearsRENT100000.00Source Verified2011-12-01defaulterotherOther Loan7.061991-05-0114.00.011997.055.529.0fINDIVIDUAL951CA0.02.0325.740.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN18.427530-66.25378920
51000.036 monthsDD1Internal revenue Service< 1 yearRENT28000.00Not Verified2011-12-01non defaulterdebt_consolidationDebt Consolidation Loan20.312007-09-0111.00.06524.081.523.0fINDIVIDUAL641MO0.01.036.320.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN18.263085-66.7129854
610000.036 monthsCC4Chin's Restaurant4 yearsRENT42000.00Not Verified2011-12-01non defaulterhome_improvementHome18.601998-10-0114.00.024043.070.228.0fINDIVIDUAL921CA0.02.0370.460.061.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN18.392282-66.08855513
79200.036 monthsAA1Network Interpreting Service6 yearsRENT77385.19Not Verified2011-12-01non defaulterdebt_consolidationlowerratemeanseasiertogetoutofdebt!9.862001-01-018.00.07314.023.128.0fINDIVIDUAL921CA0.00.08061.100.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN18.392282-66.08855510
810000.036 monthsBB3Wells Fargo Bank5 yearsRENT50000.00Not Verified2011-12-01non defaulterdebt_consolidationDebt Consolidation16.012003-04-016.00.017800.091.817.0fINDIVIDUAL917CA0.00.04942.630.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN18.420674-66.0501058
97000.036 monthsCC5GREG BARRETT DRYWALL7 yearsRENT34000.00Source Verified2011-12-01non defaultercredit_cardCredit Card Loan6.352007-10-016.00.06113.060.56.0fINDIVIDUAL934CA0.01.0260.740.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN18.411313-66.1242344
loan_amnttermgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspurposetitledtiearliest_cr_lineopen_accpub_recrevol_balrevol_utiltotal_accinitial_list_statusapplication_typezip_codeaddr_statedelinq_2yrsinq_last_6mthslast_pymnt_amntcollections_12_mths_ex_medmths_since_last_delinqmths_since_last_major_derogmths_since_last_recordopen_acc_6mopen_il_6mopen_il_12mopen_il_24mmths_since_rcnt_iltotal_bal_ilil_utilopen_rv_12mopen_rv_24mmax_bal_bcall_utiltotal_rev_hi_liminq_fitotal_cu_tlinq_last_12mtot_coll_amttot_cur_ballatlngage_of_credit
496918000.036 monthsCC1Repair Tech< 1 yearRENT39000.0Verified2015-01-01non defaulterotherOther15.512005-10-0113.00.07673.074.520.0fINDIVIDUAL982WA0.01.06376.780.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN10300.0NaNNaNNaN0.033175.018.411261-65.99204510
496925000.036 monthsBB3Qc inspector5 yearsMORTGAGE46000.0Verified2015-01-01non defaultercredit_cardCredit card refinancing8.102007-02-0111.00.0106171.022.022.0fINDIVIDUAL917CA0.00.04310.110.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN148400.0NaNNaNNaN2242.0243255.018.420674-66.0501058
496932000.036 monthsAA5medical biller8 yearsMORTGAGE31000.0Source Verified2015-01-01non defaultercredit_cardCredit card refinancing8.452004-11-0113.00.04043.016.948.0wINDIVIDUAL918CA0.01.01785.691.062.062.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN23900.0NaNNaNNaN4151.04043.018.421022-66.06578911
4969435000.036 monthsBB1Manager1 yearOWN185000.0Verified2015-01-01defaultercredit_cardCredit card refinancing8.721995-01-0110.05.06341.077.328.0fINDIVIDUAL926CA0.00.01107.630.037.047.045.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN63200.0NaNNaNNaN1043.0835947.018.345400-66.05154520
4969526850.060 monthsGG2Operations Admin10+ yearsMORTGAGE82500.0Verified2015-01-01non defaulterdebt_consolidationDebt consolidation20.332004-01-0118.00.016963.047.325.0fINDIVIDUAL907CA0.02.025224.420.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN35900.0NaNNaNNaN0.0465998.018.452553-66.07783811
4969615000.060 monthsCC1Merchandiser3 yearsMORTGAGE45000.0Not Verified2015-01-01non defaultercredit_cardCredit card refinancing31.441995-06-0114.00.014249.075.834.0fINDIVIDUAL740OK4.01.015103.250.05.012.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN18800.0NaNNaNNaN0.087729.018.331178-65.63410420
4969730000.036 monthsCC5Teacher10+ yearsMORTGAGE84000.0Verified2015-01-01non defaulterdebt_consolidationDebt consolidation16.542003-07-0115.00.030246.070.245.0fINDIVIDUAL920CA0.01.0140.760.062.062.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN43100.0NaNNaNNaN0.0302480.018.414292-66.08804212
4969812000.036 monthsBB5utility Pre-Craft Trainee1 yearRENT65000.0Source Verified2015-01-01non defaulterdebt_consolidationDebt consolidation8.491993-09-0114.00.012987.044.625.0fINDIVIDUAL907CA0.01.09178.830.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN29100.0NaNNaNNaN0.019865.018.452553-66.07783822
4969912825.036 monthsDD4Sales Associate6 yearsMORTGAGE38000.0Not Verified2015-01-01non defaulterdebt_consolidationDebt consolidation9.032006-06-018.00.011878.070.724.0fINDIVIDUAL786TX0.00.012170.640.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN16800.0NaNNaNNaN0.0131124.018.155424-66.2299079
497004000.036 monthsBB1Lead Custodian10+ yearsMORTGAGE50000.0Verified2015-01-01non defaultercarCar financing12.632002-09-0111.01.01700.05.630.0fINDIVIDUAL956CA0.00.03655.510.0NaNNaN84.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30100.0NaNNaNNaN0.018979.018.321137-66.17041913